{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"-1\">\n",
    "Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits. Leverage can work against you. Due to the certain restrictions imposed by the local law and regulation, German resident retail client(s) could sustain a total loss of deposited funds but are not subject to subsequent payment obligations beyond the deposited funds. Be aware and fully understand all risks associated with the market and trading. Prior to trading any products, carefully consider your financial situation and experience level. Any opinions, news, research, analyses, prices, or other information is provided as general market commentary, and does not constitute investment advice. FXCM & TPQ will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on such information.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The author is neither an employee, agent nor representative of FXCM and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of FXCM or any form of personal or investment advice. FXCM assumes no responsibility for any loss or damage, including but not limited to, any loss or gain arising out of the direct or indirect use of this or any other content. Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_tick_data_reader as tdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(tdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 6, 25)  \n",
    "stop = dt.datetime(2018, 6, 30)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "td = tdr('EURUSD', start, stop)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1963779 entries, 06/24/2018 21:00:12.290 to 06/29/2018 20:59:00.607\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype  \n",
      "---  ------  -----  \n",
      " 0   Bid     float64\n",
      " 1   Ask     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9+ MB\n"
     ]
    }
   ],
   "source": [
    "td.get_raw_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1963779 entries, 2018-06-24 21:00:12.290000 to 2018-06-29 20:59:00.607000\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype  \n",
      "---  ------  -----  \n",
      " 0   Bid     float64\n",
      " 1   Ask     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9 MB\n"
     ]
    }
   ],
   "source": [
    "td.get_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:12.290</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:16.046</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:22.846</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:22.907</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:23.441</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Bid      Ask\n",
       "2018-06-24 21:00:12.290  1.1662  1.16660\n",
       "2018-06-24 21:00:16.046  1.1662  1.16650\n",
       "2018-06-24 21:00:22.846  1.1662  1.16658\n",
       "2018-06-24 21:00:22.907  1.1662  1.16660\n",
       "2018-06-24 21:00:23.441  1.1662  1.16663"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.get_data().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = td.get_data(start='2018-06-29 12:00:00',\n",
    "                  end='2018-06-29 12:15:00')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.011</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.071</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.079</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.091</th>\n",
       "      <td>1.16495</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.205</th>\n",
       "      <td>1.16496</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Bid      Ask\n",
       "2018-06-29 12:00:00.011  1.16497  1.16498\n",
       "2018-06-29 12:00:00.071  1.16497  1.16497\n",
       "2018-06-29 12:00:00.079  1.16497  1.16498\n",
       "2018-06-29 12:00:00.091  1.16495  1.16498\n",
       "2018-06-29 12:00:00.205  1.16496  1.16498"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Mid'] = sub.mean(axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['SMA'] = sub['Mid'].rolling(1000).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub[['Mid', 'SMA']].plot(figsize=(10, 6), lw=0.75);\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Candles Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_candles_data_reader as cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(cdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 5, 1)\n",
    "stop = dt.datetime(2018, 6, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`period` must be one of `m1`, `H1` or `D1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = 'H1'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = cdr('EURUSD', start, stop, period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = candles.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1080 entries, 2018-04-29 21:00:00 to 2018-06-29 20:00:00\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   BidOpen   1080 non-null   float64\n",
      " 1   BidHigh   1080 non-null   float64\n",
      " 2   BidLow    1080 non-null   float64\n",
      " 3   BidClose  1080 non-null   float64\n",
      " 4   AskOpen   1080 non-null   float64\n",
      " 5   AskHigh   1080 non-null   float64\n",
      " 6   AskLow    1080 non-null   float64\n",
      " 7   AskClose  1080 non-null   float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 75.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BidOpen</th>\n",
       "      <th>BidHigh</th>\n",
       "      <th>BidLow</th>\n",
       "      <th>BidClose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 16:00:00</th>\n",
       "      <td>1.16768</td>\n",
       "      <td>1.16820</td>\n",
       "      <td>1.16731</td>\n",
       "      <td>1.16769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 17:00:00</th>\n",
       "      <td>1.16769</td>\n",
       "      <td>1.16826</td>\n",
       "      <td>1.16709</td>\n",
       "      <td>1.16781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 18:00:00</th>\n",
       "      <td>1.16781</td>\n",
       "      <td>1.16816</td>\n",
       "      <td>1.16668</td>\n",
       "      <td>1.16684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 19:00:00</th>\n",
       "      <td>1.16684</td>\n",
       "      <td>1.16792</td>\n",
       "      <td>1.16638</td>\n",
       "      <td>1.16774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 20:00:00</th>\n",
       "      <td>1.16774</td>\n",
       "      <td>1.16904</td>\n",
       "      <td>1.16758</td>\n",
       "      <td>1.16816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BidOpen  BidHigh   BidLow  BidClose\n",
       "2018-06-29 16:00:00  1.16768  1.16820  1.16731   1.16769\n",
       "2018-06-29 17:00:00  1.16769  1.16826  1.16709   1.16781\n",
       "2018-06-29 18:00:00  1.16781  1.16816  1.16668   1.16684\n",
       "2018-06-29 19:00:00  1.16684  1.16792  1.16638   1.16774\n",
       "2018-06-29 20:00:00  1.16774  1.16904  1.16758   1.16816"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.columns[:4]].tail()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AskOpen</th>\n",
       "      <th>AskHigh</th>\n",
       "      <th>AskLow</th>\n",
       "      <th>AskClose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 16:00:00</th>\n",
       "      <td>1.16769</td>\n",
       "      <td>1.16820</td>\n",
       "      <td>1.16732</td>\n",
       "      <td>1.16771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 17:00:00</th>\n",
       "      <td>1.16771</td>\n",
       "      <td>1.16827</td>\n",
       "      <td>1.16711</td>\n",
       "      <td>1.16782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 18:00:00</th>\n",
       "      <td>1.16782</td>\n",
       "      <td>1.16817</td>\n",
       "      <td>1.16669</td>\n",
       "      <td>1.16686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 19:00:00</th>\n",
       "      <td>1.16686</td>\n",
       "      <td>1.16794</td>\n",
       "      <td>1.16640</td>\n",
       "      <td>1.16775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 20:00:00</th>\n",
       "      <td>1.16775</td>\n",
       "      <td>1.16907</td>\n",
       "      <td>1.16760</td>\n",
       "      <td>1.16861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AskOpen  AskHigh   AskLow  AskClose\n",
       "2018-06-29 16:00:00  1.16769  1.16820  1.16732   1.16771\n",
       "2018-06-29 17:00:00  1.16771  1.16827  1.16711   1.16782\n",
       "2018-06-29 18:00:00  1.16782  1.16817  1.16669   1.16686\n",
       "2018-06-29 19:00:00  1.16686  1.16794  1.16640   1.16775\n",
       "2018-06-29 20:00:00  1.16775  1.16907  1.16760   1.16861"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.columns[4:]].tail()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['MidClose'] = data[['BidClose', 'AskClose']].mean(axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SMA1'] = data['MidClose'].rolling(30).mean()  \n",
    "data['SMA2'] = data['MidClose'].rolling(100).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mdKqOUbEjyLMfosnTTJTePNihCSGEEMeFmBgLdXW1PPnkE8TEWCgpKeb223/K/v37yMvbz6uv/pvvfOcGnnvuJQAKCwtYu/ZL8vMPs27dGjZv/obi4iIcDgcWi4XKykpUVeWVV14iOTmVv/zlUbxeL//zP/cDEB0dw/z5Zw9mlwFQhtrTchUV9UMmoJQUK/c9/RU7D1bz3C/n8WH+Sj7O/4KfTr2V8YljBzu8sImkndojpa+R0s+BECn3MlL6CZHT10jpZzj05V6lpFiVI52T6cJuJFhNAJTbm8iOa1uXJYQQQghxNJJkdSMnI7B4LrfQTnb8aBQUbDW5gxyVEEIIIYY6SbK6kRIfBUBtgwuLIYbRsZkcqD1Mo7tpkCMTQgghxFAmSVY3jC2bR7tbtt6ZkDQen9/H3pr9gxmWEEIIIYY4SbK6YWhJslr3Nzw5SQNgV+XeQYtJCCGEEEOfJFndMARHsrwAZFozsBhi2FO9T/YxFEIIIcQRSZLVDaNeB7RNF6qKyvjEsdS66ihuKB3M0IQQQggxhEmS1Q2DIXS6EODkxMCU4Z7qfYMSkxBCCCGGPkmyutFx4TvA+MRxAOypkiRLCCGEEF2TJKsbwYXvbm/wWJzJSoYljdzag7i8rsEKTQghhBBDmCRZ3dCpKjpVCRnJAjgpcRwen4f99oODFJkQQgghhjJJsnrAoFcprmrA421LtE5qnTKstg1WWEIIIYQYwiTJ6oGJ2Uk0Ob3sPVwTPJYTn4VRNci6LCGEEEJ0SZKsHpg7OQ0AW4E9eMyg6hmXkENpYzmVTVWDFZoQQgghhihJsnpgRKoFgJKqxpDjU1ImArCl/Ntetdt86CB132zoW3BCCCGEGJIkyeqBuBgjUSYdew7XhDxlODllAqqisrWXSVbNx6soffF5GvfJui4hhBDiRCNJVg8oisKk7CSanB4OldYHj1sMMWgJY8ivL+rVlGH82ecAUPHaf/H7fN1cLYQQQojjiSRZPTQiJTBl6Gw3kgVwSuoUoHdThlHZOVhnno7z8CHqN6zve5BCCCGEGDIkyeohszGwh2FpdSPedqNOU1qmDLeU9W7KMPnKRSgGAxXL3sDndIYlViGEEEIMPkmyesjUkmT995P9PPv2zuDxaH0U7ppEChy9mzI0JCWRcO75eO127F98FrZ4hRBCCDG4JMnqoSijPvh66/7K4OvaBhfe6uEAbCzZ3qu2E867ADUqipqPPsDX3Ny3QIUQQggxJEiS1UOtI1kdlVQ14q1Jxe9TeHfXOt5de+zb7OgsFuLPOQ9vfT32zz7pa6hCCCGEGAIkyeoh8xGSrPpGF3iN+OqSUGPqWL5xR6/aTzj3fNToaKpXfoi3qakvoQohhBBiCJAkq4dMhtAky+/388K7u3h++S4APBUjAtcNL+xV+7roaBLOuwBfQwP2T1b1LVghhBBCDDp9dxdomjYceBiYYrPZZnRx/l5gOFAKnAo8YLPZ9racuwGYBniBPJvN9kIYYx9QZlPorWpyevl6d1nwvc+eit9jQEkoxe/3oyjKMX9HwjnnUvPJKmpWfUT82eegi47pc9xCCCGEGBw9GcmaAywHjpQ1WIBf2my2x4C3gMcBNE0bAfwK+JXNZrsH+IGmaWP7HvLgMHcYyWp2eUIv8Kt47SlgaCa/PnQ0y5Zfw6uf7sfv9x/1O1RzFIkXXISvqYnqD94PS9xCCCGEGBzdJlk2m+1NoP4o539ns9laswcVcLS8Ph/Y3O7ceuDCPsQ6qDquyWp2eTtd460ZBsDW8tB1WY/9ZyurNhZwoLiu2++JP+tsDMkp1Kz6iOZDh3ofsBBCCCEGVbfThT2laZoRuAm4o+VQKqHJWV3LsaNKSIhGr+96kflgSEmxAnQahTKajSHvZ01KY/1OLzq/gS0V21k8cxGqGprDRltMwfaOzIrpZ3ew63cPUfDYI+ijo1FNRlSDAdVoRDUaUdq9Vo0GVEP71wZUk6nlegOxE04mZvToHvczEkRKXyOlnwMhUu5lpPQTIqevkdLPcOiPexWWJKslwXoO+K3NZstrOVwOjGl3WSyQ211bNTWN4QgpLFJSrFRUdD2It3VPKQA6VSHBauKqM7NYv6MEq3sUVU25rNq9jlOHTQn5TE1NY0h7Pr+f3MJaxo6IC13DlTaalGuuo279GnxuN163G0+zA7/bHfjl6TBVeRSKXs+oB3+PMS29V/08nlXXNWM26og2G4LHTtS+dhQp/RwIkXIvI6WfEDl9jZR+hkNf7tXRkrNeJVmapiUCHpvNVqdpWhTwLPBnm822S9O0q2w221vASuCnmqYpLVOGs4CnevN9Q9F/P90PwKL5OZx32kgamwOJT0LjydQa8/gkfzWnpE4OSZ4OldYzLjOe/1tpo7qumYnZSbz2WSDv/Ps989G1G/lKOO98Es47v8vv9vt8wYTL15p4ud343a62Yy4XruIiKpe9Sekr/yDznvtQ1Mh5mNTj9fGrZ9eRYDXxxB2zBzscIYQQEagnTxfOA24E0jRNux94Avg1UA08CiwFJgJZmqYBxABv2Wy2Qk3T/gz8VdM0L7DEZrPt759uDIwok54mZ+goUutThzpdIJlSPRYmJZ/Mt5W72FG5m8kpE4LXvvlFHpW1zXz1bQkA5fa2elhVtc2kJkT3KA5FVVFMJjCZOOrE6tRpNOcfxrFpI/bPPyXh7HN71P6JoL7RDUBNvewHKYQQYnB0m2TZbLbVwOoOh+9pd/7Ko3z238C/ex3dEPOXO2bj8/vx+f389G9fATAlJwkAfUuS1dDk4fD6VBi7mw8OfcJBW1RIG19sLQq+rrC3baFTXefscZJ1LFKvu4HGPbupXPYmlilTMSSnhP07hqK6BtdghyCEECLCRc78URiYjDqiTHpizAaeuGM2z901jziLCQC1ZVrwYEkd9TUmvNWpFNQXsWLP2h607Ke8n9ai6ePiSP3O9fidTspe+We3ZSROBDX1TjbvqxjsMIQQQkQ4SbJ6KcFqCqkCryhKcDQLwF2cg0lnxJizA9PENegz94LaxYJ1vQvT5K94q+oF1hVv7JckyHr6LGImTaZxzy7q1n4V9vaHmrueWcuKdYcGOwwhhBARTpKsMGq/cN3fGMuvTv0JXnsKiqkJQ9ohTBPXoUTXAnDGxOH88YenM/HMIlRzIx6lmaV73+Cfu/+Lz+8La1yKopB6402oZjMVr7+Gtz5ynjaJsxi7v0gIIYToB5JkhZFODS2K72+y4Np3KmPsi5g9fBaquRHjmO2geFl01hh2OzaR12ADRyLxBRcwKjaTTWXb2FG5O+yxGRKTSLrsCnyNDVQseyPs7Q8VHR9M8HjCm7AKIYQQPSVJVhi1ny4EWL29GIC5k0bw3ZOvIKpuDKq5Ef3wQ+ys2c5buSsAMFZMwtNk5jrtKgA2lGzul/jiF5yDMWMEdV99SVNetyXLjkutpTRaNTR7+GxL7zbtFkIIIfpCkqww0ulCb2d+aT0KMHVsMgBpnin43UYMmfv5z743MKh6rhl3OVHeJJwuL5nWdEZaM/i2chdby3fg8Xnw+rx9XqfV5PTg8fpQdDpSr78RgLJXXsbbOHQKv4aL29t55Orfq/YNQiRCCCEiXdi21RFg6JBk1Ta6sUQb0LccT7HGs3vPaRhzviUxUeG2STcxMnYEXxg3Ul0fKOdwzbjLeXLb31my8/9C2lJQUBQFFQVFUVEVBYXA76qidjinohI4nmnJZM0HKUwfN4wfXzGJ6HEa8eeci/2Tjyl+6m+k//RnwImz7YLLHdhT8uxTR/DpZhnBEkIIMXgkyQojU4dNpOsaXCTGmoLvo016/M0WnLtm8fC9C4LV4M0GHS63j/KaRtItI7hjymKW5a7AqBpQFBW/34fP78ePr6VOlw8/Lb+3vPfR+jrw3oMPl6eJzRVbMeaksmn/ZGASACnXXIe3tpb6jd9Q9NcnSH7koYG6Rf3O3bIGy6iXQVohhBCDS5KsMHJ1WGTd5PQQG902SmQ0tP7Fr4Rst9OanP36hQ3ERhv439vP4J7pP+17PF43d773BLrEckzmDTR55hKlN6OoKsNv/RGKwUDdurXsvP9Bhv3kF+hjY/v8nYNt+dqDABj0KrExRuoaXChKNx8SQggh+oH8cz+M7F1s4RIT1bY5sVHf9SY4Mea2XLeu0U1uUS3OlmmvvjDqDHj2TcdTPgI12sHre5cHzymqyrCbFxM3dz4NBw5S8NgjuCuP3wKehRUOvv/oZ+w8UA2AXqfy0C0zAIiNkTIOQgghBp6MZIWRp4tF1+Z2BUvbRrJCnTk5nfW7yoLv//zqNgBOGpXASaMSiLeYiLcYibeYiLMYsUQZQkbCjibGbKL+0Mmo1ho2VWzlcueFxJkCI1aKqpJ6401YUxMpfHMZ+Y8+wohf3I0pI6PHfe6tPYeqsUYbGZFqCUt7Hddfebw+YmMMxJ68A5e5gq9LopiZdmpYvksIIYToCUmywsjr6/wUYPt1Wnpd10nW+FEJXR7fc7iGPYdrOh3XqQrxFiNxFlMw8Yq3mIiPaT0WeH+4rL5lo2QVT+ko1KzdrCv+hguzzgm2pSgKo268nmbFQOUbr1H89N8Y9dDDqCZTp+8NF5/fz+MtieTLv14Qlja93tB77/b4WFO0AbclsFfkv/a8htPr5IrkyNkkWwghxOCSJKufmdslWUcrxbDorBze+Dyv0/GfL5pCrcOJ3eHE3uCi1uHC7nBS63ByuLSeA766HsXhrUpHl7WPDaWbuWD02Z1GwhLPvxBvXS01Kz+i8p1lpF57XQ97eOzaFwx956sDXH5mdp/bbGh2h7x3ejx8cPATFJ8eV+40Eibs5rV97/BJ4Wpu0K5hXEJOn79TCCGEOBpJssLojInDWbezNORYSJJ1lM9eOHMUHo+Pt786GDw2Z1Iak3OSjvgZn99PQ5Mbu8NFrcNJjcPZLglzhW6S7NOTTBZlTfvJqz3EmPisTu0lXXYljq1bsX/6MXFz5vbbtKGjqS0henftobAkWR2LkNZ4S6l3O7A0jaHRnsQvTvkxqw5/xqayrby8cym/nflLrMbwTFUKIYQQXZGF72F00wXjefgHM0OOqe222umupmj76cRFZ+XwvQu0o16vKgrWaCOZqRYmZidx5uR0Fp4xmhvO07jjykk8+qNZACS1lJGwNgcSq23lO7puz2gk5drrwOej4tX/9Mtm1RCaZHX1vjcamt1Em/TMmZwGQGJGYIQvxhNIFJNMiXzv5Gv57uQrqHc7+K9tWZ+/UwghhDgaSbLCyKBXSU+OCTlWaW9ue9NN0tJ+78OkWPMR13D1VGp8FH+67XTuvynwlJ2vPgGzzsymsm00e5q7/EzM5ClET5xE455dNGzb0qfvP5KGDklVZW1Tn9u0O1xYY4x8/6KTeOneszjQsA+9qifWOxwAtydw7y/WFjAmPovtFTvZWbmnz98rhBBCHIkkWf3s7FNHBF/POGkYqQlR/OiyCV1e235bHqOh63IPx2pYQjSx0QZURcHpgrNHnkm928En+V92eb2iKIH1WDodFa+9is/ZuSxFXzV22MQ5sDj/2Lg9Pg6X1rN2RwlLVuzG0eQmuWXErsBRRGljOZOST8aoC5Rv8PgCT36qisq1465AVVTe2v8ePr9sIC2EEKJ/yJqsfvTgzTNCRrYsUQYevW3WEa/Xtdtg2hymJAsCiVOUSUeTy8PZI+fxVdEGPs1fzZyMmcSb4jpdb0xLJ+Gcc6lZ+RFl/36F4d+/tcclI3rC6QqtAWZ3HDmR8/v92B0uCsodFJTXU1jRQGG5g5KqRnwdRgaz0gN9+aY0MAJ32rBpbDgQOOdpVyg23TKcU1Ins6lsGyUNZWRY0sLRLSGEECKEJFn9KCbq2G5v++nCjlv09JXZqKPZ6cWkM7Iw6zz+Y3uL9/JWcuPJ13R5fdLlV9G0bx/169dhzsomYcE5XV7XGx2TrH98sJczJ6fjcnspqgwkUQUVjsDv5Q4aOixqNxl1ZKfHMiLVQmZKDBkpFppdXsaOiMPr87KpbBsx+mhOTtLYH3cYgPxyB4mx5mAbWsJYNpVtI9d+UJIsIYQQ/UKSrH4UYzZ0f1E7erVtutAc7iTLpA9WpD89bTpfFK5lQ+kmqptr+O2Cn3S6XgxVYR0AACAASURBVDUYSLv9J+T/4UEqXvsv5syRRI0dF5ZYNtrKOx377d83UFrdGLJsTQFSEqIYPzKBzFQLI1p+JceZUY8wsrajcjf1LgdnZsxCr+o5aVQC768/TF5RLVPHJAeva326Mtd+gHkjzghLv4QQQoj2JMnqR8eaKLWfLjSFcbqwNZZmlxe/349O1XHLhO/y771vsM+ex8tbXuOa7Cs7fcaQmEjabT+m8C+PU/z8M4z63UPo47sunNpTTU4PeUWBJ//u/9509hXYef3zXOwOF2Mz4oKJVGaqhYzkGMzGnv+I+vw+3sn7EAWFM9IDi/1bp2tLqxtDrk2JSiJKH0Who7hP/RFCCCGORJKsfvCLa6ZgdziPeR2Trt1IVrinC6OMerw+Py6PD5NBR7plOL885Xb+vOkZVh/awKioUV1uOxM9/iRSrr6Witf/S/Hzz5J5z30oau+flyipakt2TEYd55+WyexJw49pq6Aj2VCyidKGMmYOP5WR1sADB3ExRkxGHaVVjby1Oo/zz8jCYlBRFIUMy3Dy7IdweV3BBfJCCCFEuMjThf1gUkvNqmPVnyNZlujA1GV9gyt4TK/q8R+eht+r49W9y3G4G7r8bPy552E5dTrNufupW/tVn+Ior2lLsoz6QLJjjTb2OcGqdzl4J/cDTDojl+ZcEDyuKAqx0QaKKht4f/1h7nzii2B1+PSYNPz4KWkoO1KzQgghRK9JkjWE6NslWX2tkdVRUsui73J7E+X2Jr7/6Gd8urmQ/XkePEVjcPmbWXnosy4/qygKKd+5HsVopHLZW3gbuk7GeqL9IvbY6PCNHr2d+z4NnkYuyb6g0xOTHdfGvfLhXpqcHtItgRpaRY7QKv1CCCFEOMh04RCi68M0XHcSrYEaUs+9s5MLTx8FwNKP9wHgKRtF3Ohivixaz4LMM0kwx3f6vCEhgaRLLqPyrTeoeO2/DP/+D7r9To/Xh98fKNLaqtkVSLLuvGpyl1OiXp8Xl8+F0+vC5XXh9LpxBV87A8fbnXd53TS4G/m6dDOZlnTmZnQukRETFZpkbbJVUFXXzI1XBEYbC+qLgBnd9kcIIYQ4FpJkDSFGQ/8lWSePTgQCI0kud2gJBfwqY/Uz2O76jI8Of8Z1WudF8AAJ511A/cZvqFu3Bsv0GVgmT+nyuvKaRg6V1rPymwLsDif/e/usYALZ5Ax8d+v05baKnSzb/x7NHidOrxOP39tlm90x6ox8Z/yV6NTOiVtUF8ncwZJ6LJyEqqgtSZYQQggRXpJkDSFZw2O54sws4iymsLc9LDGaOZPSWLOjhEOl9Z3O79kWQ9zUWLaWf8u14y5HVTonfIpOx/BbfsDhhx+i7J8vYX7oYfSxsZ2uu3/J13i8bbUYNtsqOO2kYUDg6UIIJD6N7ibezfuQquYahkWnkKJPxqgzYtIZMKpGTDojxpZfra9Nauix1uPxpjhiDNFd9r21PlbHDbz/5+UtpJ0xjEJHMV6ft8sETQghhOgtSbKGEFVVuGR2Vr+1b2mZNiur6bxXYK3DTY4xiz2O7Ryqyyc7bnSXbZgyM0m+8moq33iN8v/8m/Qf/bjTNe0TLICPNxawelsxt18+kaaW6cIok5538lZQ1ljBnIzTjzh6Fg5Xzcth/MgExo+KD0myGpo9ZFozKHKUUNZYEVyjJYQQQoSDLHyPIK1TdK1P+LWvMA+wbUtgJGdHNxsnJ5x7PuacMTg2fUP9ls3dfm9ecR17Dtfw0df5NLdMF+r1sKV8B7FGK9eMveyY+3IsDHqVqWOTu6y51VrqIb++sF9jEEIIEXkkyYogrSNZrVXVH7l1JosvPil43leXhE7Rs7ObJEtRVYbddAuKwUDZP1/GXVPTo+//YMNhahucKMChhgM0eZqYPmzqoEzT6X0exjnyyajyovP6KXKUDHgMQgghTmySZEWQjqUMUhOimT0pjfNmBp42xKcj2j2M4oZSqpqqj9qWKT2DlGuvw9fYQOXr/+1xDAdL6ok269lesROAU1InH1snwiDG08T1RSu5svQLfE+9xO1vVJD5j5VULn8bV2kJ/g4bTwshhBC9IUlWBDGbuh4xGjeybascfUNgXdLOqr3dthc3dz7m7BzqN35D457dPY4jyqxjd7UNq8HCqNjMHn8uHJJcdr5X+AFpzir2WEZhmbeAmkQz1koH1e8t59D993Hw17+i7F//wFlYMKCxCSGEOLFIkhVBzEeoIj9tXErwtaGxJcnqZsoQAtOGqTd8DxSFsqX/wud2d7rmkjNGc8HMkaQltT35Z7I2Uueq56SkcV0+xdhfGvfs5gdlq4jzNLB/3GyWD5tL7NXXsfO7s3j+6hRib7gey/QZ+Jqaqf1yNfl//AMNO74dsPiEEEKcWCTJiiDtt+oZmWoJvk5NjOYPi08DwO+MIsOSxr6aXJo9zm7bNI8cRfxZC3CXlmL/eGXn8yYd15w1hkduPZ0RKYHNmn3WwBN+ExK1PvXnWNStW0vh355A9XoYvviHFGizQFHwen2kW9Lw6BVqJo0i/Ud3kPO3p0hreWqy+Lmnac4/PGBxCiGEOHFIkhVB2ldY/9V100LOZaRYsEQZcHt9TE6egMfvZUt5z0Zxki6/Ep01lqoV7+IqC90HsP3oWetWQU2mInSKjgnJ43vblR7z+/2ULf0/Sl/+O6rJzIT/eYDYWWcEi6N6vH4yLGkAFDUEFr8rqop1+mmk3XobfpeL4qefxFNf1++xCiGEOLF0m2RpmjZc07QlmqZtPMo112ialqdp2sIOxx/VNO33mqb9r6ZpvwxHwKL32o9kWTpsNQOBUgcer49ZadPRq3qW531Ao7tzTa2OdNExpF53PX6Xi7J/vtT2+CKElE3wA4rZgdNQzbiEHKL0UX3rUA9Uv/8etZ9/ijFjBCN/cz9xEycAbftEenw+MoJ7GIY+YWiZdipJl12Bp7qKkheew+/tXTV6IYQQkaknI1lzgOWA0tVJTdOygAqgoMPxy4GTbDbbAzab7R7gek3TTuljvKIPutorsD2DTsXt8ZEUlcgFo87G4W5gbfHXPWrbMuM0LKecStP+fcyobVvPZW7/nX7QpQTqUc1Km37sHThG9Vs2U/XOMvSJSYy46x6Mw9OC53Qto2per5/kqCSMqqHLMg6JF19CzLRTaNq7h4o3Xuv3mIUQQpw4uk2ybDbbm0DnfVjazh+02Wyfd3FqLJDf7v0BYMExRyjCxqg/+h+3Xq8Gq7XPG3EGRp2R1YXr8Pq6H8FRFIXUG29CZ7Uyv3ILU2r3gd8fktj58KJPLkb1mZicMrFvnemGs6CA0pdeRDEaSf/JnZ22/9G3FGL1eH2oikqaZTilDeWd+qqoKmmLb8WYlo79k1XUfb2+X+MWQghx4ujPbXXWAldpmqYCOmAq0O0jawkJ0ej1Q2cPuZQU62CHEHbxVlOnfqWkWDGb9NTUN7ecszI/63RW5X7JYfdBZo6Y1nVjIY1Yib7vbjbe/zAXVmwgo7mCFEUjOXkkiqLgsZSgGFwkuiaQPiyh+/Z6qWr9Bgqfega/08n4X99N0qmhCV1KihWrNbCfoTU2ipQUKznJIzlcV4DL1MDI+IwOLVqJ/d19bL/rHsr/9U+GTxpPzKiR/RZ/uJyIP7uDJVLuZaT0EyKnr5HSz3Doj3vVb0mWzWZbp2naE8ADgB3YSOjIVpdqWrZ8GQpSUqxUVBxxEO+49Mwv5qLXKSH9au2n4vfjcvuC505LmsGq3C/5aO+XZJvG9OwLUkfyz8yFXFnyOZPr86j5w/1sHp1FytXXYB1Rir0JLtbm9Mt9bTqQR/WH79OwdQuK0cjwxbfiGzOhy766nIFyE5VVDhKi9CTpkwHYUZBLlLvzptcYraTevJiS555m18OPMvK++9FZh+7/vE7En93BEin3MlL6CZHT10jpZzj05V4dLTnrVZKlaVoi4LHZbEd85ErTNDOww2azvdHyfiXwdm++T4RPlOnIf+R6nYrX58fn96MqCmkxw8i0ZrCneh8OVwMWY0yPvqPWYOE/GedzSq2Nc9O8OHd9S+GfH2NaupG4+eM5LSsHAL/XS8POHTTZ9uL3evH7fODzgd/X8tqP399yzNf5mN/nh5bXXocDZ0Eghzdn5zD8lsUY09KPGGPrvo3elunRjJjAeq1iR+kRP2M9dTrOixZS/cEKip55ksy7f42iGzqjrkIIIYaWbpMsTdPmATcCaZqm3Q88AfwaqAYe1TRNAX4LjAKu1TTNbbPZVgIW4AVN0z4HDMCfbDZbVT/1Q4SBQd+6GNyH2jJlO33YVArqi9ha8S1nZszqto3WLWmcOiPrEydx/e1nopYUsPf/nicrv5zRb+6hqmE5PpeL+g3r8PRw38NuKQrREyaSeMFFRGnjUdRu1p+1LHz3+HwAR3zCsKOky6/EVV6GY9NGqj98n6SFl4YheCGEECeibpMsm822Gljd4fA97c77gYdbfrX/XCUwLwwxigHSmni4PT4MLUnWqalTeCf3AzaWbutRkrXm29AkxWTUwahRvLEgloxcL+dubqRqeWBAU42KIm7+AqwzTkONigokRqqKoijQ+lpVQVGPfE5VUJR274+xr60L/aMN0SSY4rtNshRVZdiNN9OUu5+q95YTM3kK5pGjevy9QgghIkd/LnwXx5molr0Nm5xeols2k04wxzMmPov99gNUN9eQaE5gzbclxFuNTMxK6tTG3nx7yHudqrKzcg92Vx2TZs0h6/L5NO7ZjRodQ/T4k1BNpv7vWBd0utbpQl/wWIZlODur9nY7NaqLiWH4LT+g6K9/pnTJi4z83YOoBmO/xyyEEOL4IhXfRZA1OpAo1DW6Qo5PHzYVgC3l3+Lx+nj5gz385bXtXbYRbe6ct68t/gaAM9JnoI9PIHbWbCxTpg5aggXtSzi0FU7NsATWcB2uL+z28zETJhJ31gJcxUVUvbOsf4IUQghxXJMkSwTFxrQkWQ2u4NoqgCkpE1FQ2Fa+g+q65qO2YTSE/kjVOuvYWbWHTGsGI60jwh90LwWLkfraRrLGxGcBsL8mr0dtpFx9LYZhw6hZtRJnYUH3HxBCCBFRJMkSQbEtI1l7Dtfw6NIt/PX1wGiV1WhhbEIOB+vyOVBVHry+yenp1EazK7SY54aSTfj8Pmann9aPkR+74LY67UaysuNGoyoq++w9S7JUk4nU71wPfj+Vy97slziFEEIcvyTJEkHjMuMA2H2ohv2Ftew40PYw6LSWCu3by3YFj3WcVoS2xGvulHRuvlBjXfE3GFVDcMpxqNDr2p6kbGXWmxgdm0lBfRFNnqOP2LWKnjiJKG08Dd9up9G2t19iFUIIcXySJEsEpSZEk54c0+WU4PjEsQDkNxwOHnO72xIUn9/PwZI6iisa0KkK3z1nLLHpNVQ2V3NK6pQB2Qz6WOjU0KcLW42Lz8Hn95FnP9ijdhRFIfmqRQBULnszZJpVCCFEZJMkS4RIjDXR2G4a0NeSNKREJRNviqNeLQECx9ztRoGWf3WQP7yyifxyB2NHxKHq/LyT+z6qonLeqPkD2YUeaX260NNuTRbA2IRAodSeThkCRGXnYJl2Ks15uTRs3xa+IIUQQhzXJMkSIZJizSHvnS1rrBRF4aTEcXhVJ6o1UEDU5W5bf7Vhd1uldJNBx5dF66loquLMjFkMi0kdgMiPjV4XWvG9VXbcKHSKrseL31slXXElKAqVb78VqEwvhBAi4kmSJUIkdkiy8svqeeWjvTjdXk4bfgoAutTAk3RuT1sy0X7azanW896BlUTpzVw0+pwBiPrY6YPThaEJkVFnJCtuJAX1xTS6e76Ppik9g9hZZ+AqKqT+6w1hjVUIIcTxSZIsESIpNrR21WP/2crqbcV8vLGAMfFZ6Fxx6JNKUOMqcLVLsry+tiSr3ngYl9fFwqzze7zf4UBrXfi+amMBf/z35pBka1zCGPz42W8/cExtJl16OYpeT9Xyt/F7Oj95KYQQIrJIkiVCtNbK6sjj9aEqKuaSU/D7FIxZOymuqcHt8bKvwE5dQ9uTho2GEhQUpg8fWk8Utte6JqvZ5SW3sDZksb+WMAYA2zFOGRqSU4ibdxbuygpqv/wibLEKIYQ4PkmSJUKYDV3vtGQyBrbc8TbG4ikag2J0sqLoHX7/1oe8+ZUteJ0SY6fZUMHI2BFYDENzFAtA11LxvVX7+l6jrCNQFZX8uu4rv3eUePElKCYTVSvexed09jlOIYQQxy9JskSIjhXbW7WuuXK5vXhKsvDWJaCLr6Q65Usqhn2ELqUAw6hdmLRNoPi5POfCgQz7mLVOF7Zqn2QZdAaGR6dS5CjG5z+2Rez62FgSzj0Pb10d9s8/DUusQgghjk+SZIkQ5pYRq44KyupZu6OEhmYPoOKyTceVOxlPWSYupQFj1i70wwrQ6+F6bRHjWqbchqrWpwtbdaxUP8KajsvnpqKx8pjbTjjvAtSoKGo++lBGs4QQIoJJkiVCmAxtSdZ3zh7LgzfPAGCTrYKX3t/TdqFfh7c6HffhCTTvOh3XwQksyr6KP575W87ImDHQYR+z1mKkrZpdoQvVM1s2iy50FB9729ExxJ9zHl5HvYxmCSFEBJMkS4QwtRvJmjo2mRGpMViiDCHXXHFmVsh7f0M83opMZo+YPqTXYbXXk5EsgIL6Y0+yABLOPQ/FZMb+6cfypKEQQkQoSbJECGO7kaykWBM6VeWJO2YT1+6pw6y0WK5d0Hk6sOM6p6FM1yHW+g77MI5oGckqcpT0rv3oGOJmz8FTU4Nj29beBSmEEOK4dvz8rSgGhKq0jfC0TqkZ9GpIUmUy6jj/tJHcccXEkM8qSujo0FDWcSRrx4Hq4OttuZX8/G/fYNVbKWko6/V3xM0/C4DaNV/2ug0hhBDHr66f1xcRbfHFJxHTYYrQoG8b4Wpdt3Wqlspzv5zH469u5axpGQMaY18Z9KH/vigod+D3+1EUhRfe3RWoC+ayUqMW0+xpxqw3H6GlIzOlZ2DOzqFx107c1VUYEpPCFb4QQojjgIxkiU5mT0pj6pjkkGPtk5L267ZMRh33f286syelDVh84dB+4XtirIkmp4faBhd+vz+4X2OsLhGA0sbyXn9P7Jwzwe+nbt3avgUshBDiuCNJlugRY7sky2zouszD8WpkqhWA/DIHxZUNwePRJABQ7Oj9lKF1xkwUo5G6tV/JxtFCCBFhJMkSPWIwdD2SdSLITLUA8Lc3tlPX6A4e17vigN6VcWili4rCOn0G7ooKmvbv61ugQgghjiuSZIkeMXaxJutEMXVsYGpUVZTQpwybY1FQKKwv6lP7sXPmArIAXgwdbo+XWocUyhWiv0mSJXqk/Zqs4+kpwp6It5gYlhiNNdrA88t3BY97PSrDYlIp7LC9zqqNBbz5Rc83j44aOw5D6jAcmzfhra/H7/eHNX4hjtVfX9/OL55ei6PJ3f3FQohek6cLRY8Y9Sd2Pm7Sq5RVh9bK8nj9ZFrSKW0oo7KpitToFJau2senWwIbR189P6dHbSuKQuzsOVS9/RZ5v/gpKAqq2YxiMqGaTKgmM6rJ1O69CaXlWOh7I7oYC9EnnYyil/90Re/tzbcDgfpwHYsNCyHCR/5PLXpEfwImWbMnDWftjlIsUYaQIqzJcWYqa5vxeH2MsKazsWwr+fVFpEanBBOsYxU/7yxcpSV46+rwOZ34nc34mp34mpvx2Gvxu5zQwxGupMuvJGnhpb2KQ0Q2t8fL51vb1hi+9lkuP180ZRAjEuLEJkmW6JEoY+BHZUxG3CBHEj6LLz6ZWy46CVVRQoqTjhkRR1VdM26PD61lo+uvSzYzfdjUkM+31tXqCZ3FQtriHx7xvN/vx+9y4XM58Tc78bkCCZjf5cLX3IzP2YzP6aTyzdexf/YJCedfiGqQEQhxbN7+8iAffZMffP9tXtUgRtO/9hyqJr/cwTnTR3Taq1SIgSJJlugRg17lmV/MPeEWvbdWuPf42kaRYswGDDoVt9dHpjWDnLgsdlfb2Fm5J+SzXp+/U+X43lIUJThdiPXI17nLy6hZ+RH133xN3Ow53ba7dV8FTS4PZ0w8vuqYif5RWds02CEMmKff3kmT00OlvZnrzxs32OGICCXpveixKJMeVT2xFr23crnbNoieMiYJvU7F4w0sdl807jIUFFYcWAW0JWPLVh8Y6DCJX3AuqCr2T1b2aAH9U8t2sGTFnm6vE5HBEm3s/qITRJMzsDG7XZ6iFINIkiwhALsjsOh9WGI0E7OS0OtVPJ5AkpVpTWdKykQKHEWosW3TK+2nXQaKISkJyynTcRYU0GTbe9Rr2ydhPp880Sggqosadyfiz0brP5AAfPI0rxhEkmQJAWQkxwBwxZlZABh0Ch6vD7/fzzPLdmC2B6YbDKN3g6EZVM8R2/L5/by1Oo+DJXX9EmvCuecBUPPJqqNet+zLtpG2ZteR4xWRw+XpvOvAifizUVPfNnolOZYYTJJkCQHcesnJ/PjyicwYnwqAXqfi9vqpbXCxeV8Fn69twF2chWpuJGraF5infYYuteuRrP0Fdt5ff5g/vLKpX2KNyhmDOTubhu3bcJUdecuf99cfDr5ubD7x/iIVx651X87JOW2blTc6T7yfjbyi2uBrGckSg0mSLCEIFCSdPj41+LRg63RhYYUjeI2naCzugrH4mqNQdD6Mo3ezvXx3p7Zapx77U8I554Pfj/3TI49m6dqtnzsR/yIVx665Ze1h+7p3tQ39//M60EqrG4OvJccSg0mSLCG60Lrw3dFuL0P8Kp6SHJzfzqN5x2z8PoVluSvw+rwhny2vafsffFM/JTeWU6ejT0ikdu1avI2NXV6j17X9511dJ4t/BTS3/DxeMTc7eOzZt3cOVjj9pqHdyK2MZInBJEmWEF1oLeHgbreGxWTU8fTP5xIbbcDfZMVbmUFlcyVfl25hfckm3juwkkN1+ZTZ25KeqrrmfolP0emIP2sBfmczdUfYEzE2pq2O1rd5lf0Shzi+VNibsEQZSEuK4Y4rJgKB9Ut1jS48Xh/flu/h48Nf4Pb233Y7pdWNPPnmt9Q1uNieW8mrn+4P+1ZTDe22C5JtrMRgkjpZQnTBZFDx+0NHojJTLUSb9XhbnsbyFOegSy5i6d43gtd8dOhT/AYDxnFx+BzxVNVNYkSKpV9ijJs7n6r336N61UfEnbUA1RD6eP6E0Yl8sS1Q3bvJ5e2qCRFBPF4fFfZmstNjAZgyJjl47p2vDvJV/usYsr9FUeDr0s38dOoPiTMdpWhbL77/36tsrN1RitfnZ+nH+9i4txyAeVPTSUuKCdt3tS+yKjmWGEzdjmRpmjZc07QlmqZtPMo112ialqdp2sIOx/+iadpjmqY9rmnaM5qmyciZOC6YWirc17f7F/Gw+CigbfrB74rCUxKYdvE5YpkcczpeezJ+rw5dfCWGEbnsqt3ebzHqLBbi5y/Aa7dT91Xn0az20ySeLp4qE5GlvtGNz+8nMdYEBKaTrzt7LABf5u7AkLUDvAY8VcMpaSjj4/zPw/r923Mr+XJ7SfAfKbntFqeX1YSnSOr/e2M7//PPjSFrEE/EEhXi+NGTpGcOsBzosgqlpmlZQAVQ0OH4TOBsm812r81mu7ulnVl9C1eIgdFa2d7RLskamxkPwPfOHx885ikaS9PG83DuPoPp8Wfi2jcd5/Z5pFVegN+nsrVuHa5+nHpJOO8CFKORqvffw9ccOjXpa5dXub2SZEW61im09htCx8YYUcwNGHMC/xhw7ZuG+8BkrAYLG0o24fSGb1F8c4fR1PZlFirsnZMsv9/P/62ysX5naY/ad7q9bM+r4nBpPRDop4JMF4rB1e10oc1me1PTtPlHOX8QOKhp2oMdTlUBFk3TWr/DDxzs7vsSEqLR64fO1i0pKeEbLh/KIqWf0LO+JsSZAVjdMt02bVwKly8Yh05VWDjPSnJSDI/845vAxf7Av1U2768AYO7UEUwdl8Kz62w0ph/kq4o1fGdSP23onGLFfcVlFLz2Bg0rV5C1+ObgE5KGdoUnFVWJqD/jcDsR7l1py8MPKUkxwf44LdsxTViHovPiOjwenyMRgHmjzmBF7ir2NuzhnJzut2/qCb965HIjiqp2usc19c18vqWIz7cUcelZY7ttf39BTcj7Z+9dwM2/X4VOr+vyz+9E+DPtiUjpZzj0x73qtzVZNpstV9O0F4E3AB/wCYERr6Oqqen6SanBkJJipaKifrDD6HeR0k/oeV/9HUZ+Ljp9JNVVbeUcvF0UcNzQ8i/uCaMTaGpy4SnOwZpRxge2z5mdPAuz3tzH6LtmOnMBhs++oOS9FZSW2tk3cQE3XTaZxnajcA1N7oj5Mw63E+W/j6KW4riKz09FRT3NnmY+OPwuqF5ceZPwVmUErz05ZgofKJ/w/t7PmGyd3OON0I+muPzI97CypjHkHnu8Pn7z4obg+57c/x37ykPee5rdqAq4XJ5Onz9R/ky7Eyn9DIe+3KujJWf9tkZK07RLgbNsNtsVNpvtKiALuLW/vk+IcDJ32H7EoAv9TyWm3ZRLV5816FTw6ckyTqLZ28wL376C3Vl7xM/0hWqOIvPe32AckYl/4xr0r/6dgzvzQtaiyJos0dAcSLpjzIF/W3906DPq3fVcNPqcYII1Z1JgI/EoNYbJyRMocpRwoPZw1w0eo6MVxO1Yx+2Fd3dRWXtsT+YWlLX9I+jR204HAhuvy5IsMZh6NZKlaVoi4LHZbEfbNyQTaD+ZXgL0zz/lhQiz1oXvrQz60CTLGh2aZF139lj+++l+IPA0U2uNqtG6qfgTqtlbs5//t+UFTht+CnpVj07VoVf06FUdOkWHXtUFjiuhv3c8b9QZsRg6P4Wlj0/Ad9NPyPvrk+Q0FlH4wG/Iyp7BDu8omnUmWZMV4V75aG9w6jsuxojb5+GLwrUkmOI5b/RZrE/ajNvjC/6cu71+5o2YxbaKHXxZtI6c+NF9juFofP+OUAAAIABJREFUNeM6nutY1233oWpOHp3Y5Wddbi8ffp3P2p2lGA0qT955JsaWNZWKInWyxODqNsnSNG0ecCOQpmna/cATwK+BauBRTdMU4LfAKOBaTdPcNpttJfBPYJamaY8AXiAOeKFfeiFEmMXFhJZD6JhkxUaHnp89KY11O0s5XFZPvMVIXWNgwbDfp+MnU3/Am/vf5YvCtaw4ePT9Bnviu9pVzM6Y2en4gSoXb6QtYLzjMGdXbiQ7dwM/VjaxzzKSbxvHUFF1EsmJVrYcyqeJWmaPnhSWaSAx9OQV1bJlXwVXzc9BVZRgggWQYDVxqPYwbp+bqakTMeoM/H7xaaSkxPLMa1uBwMjn2KQchscMY2v5Dq4aW0+ssW/rVVpHq/7yk9n88um1Iecamp3srNxDg7uRySkTghs8q34foxuLWfH3g4x/4AZUk6lTu2t3lrJ8TWC57+knDwsmWACqosjCdzGoerLwfTWwusPhe9qd9wMPt/xq/7kG4IYwxCjEgBuRGlrbytDFwxh/uu109uXbibeaiDbrufu6aRRXNjBymBVbfmARrsfjQ1EUrh57KaenTafB3YjH58Hr9+LxeVt+93TxuqtjXrZX7OKdvA+YkjIRizF0RKusugkUhb3W0RyOHcF5aiHpeZuZWH+AifUHqLl3FVX6/8/eeYa3cZ1p+x70DoJg7xIpUd3qsrpc5d7txOlO2RRnk033l2SzSTa9XZvEsZ3Ejp3EcWLHvUq2itVlWb1YIimJYu8EQIDowHw/BgABdpGgmufWpYvA4MzMGWCAeeY973leJS35Kk4Va4lc/QFWlyyfuDdR5rzx47/vA2B6mY1Zk+wpr9nMOg40nwJgakY5AEqFAqVCSIpkSeftqsKlPFP9Ijua9nD9pKvG1SdfIIxKKZBh6ieUFGFOG97k4cNOqS8nlAjZmSyqFVjaVoMhKkW1Tn/tXYyXzcM0fwGmufMQlNJ3Mhjqm7UY9wADaVahkHMKn8oKLB5X32VkxopsRiojMwhZltSR7f7DgwC5NgO5NkPiuUGnoqLICvQNF76y8wwWo4amDg8fXVs57sjRpoZtPFfzCi+fXseHpt2Z8lrcXb4o20hTZy8vRcqgtJQifzuzPdVkaFqx+INMbQgwtSFA46m/cuY/LJSVzh5Xn2QuXFyeIJEkL4855Xb0WiXVjpMICFRkTE5pr1JK52e80sHivPm8dOp1tjXtQqfSolaoUCvUqJXqvseK2ONBlqkUqsQ57/WHMWgHXnJUBadQmp1EnNlcPauS2pYTTDl8ihktfnwaBfvz7QQiRlb6enG/swv3O7tQZ+eQdfudmBYtTuSagVSVIU69uxEKTuABWntXkmfMSdv7KiMzWmSRJSMzCGp13/Dgtz+yAMVZiqPkuoH/eKsagOsvLyU7Zmg6VlYXLmNH8x52Nu9hZeFSis0FidfiwzEmvbrP5VoQaNRn0z63HqXVTLgzF1tVISud+5nS1o7nZ7/B8c0HsE2qHFe/ZC5Mev1hzrRIM6YWVmbzhdtnE4gEOdPTQLG5EIM69XyMR7L8sdmzepWOy/MXsaVxB8/WvDymPsSFl69Qg7FVGuZeM7eAtw82I2h70RbUEw7oiFTNZnKPi7kH64l6/LRZtLx+RSY9xgjgZ9q0e7ksnI1ry9v07NhGy58exrBtK6GSZYl9aZOGCo90Hk88PtxxTBZZMucFWWTJyAxCsqjqn481GlSDrJOOYtFKhZK7ptzMgwcf5cWTr/HFuZ9ORAr8gTA6jZKyPAsn6p196+TWo7R2EXFkEzo9l3a1wHNZa/nQovco2bCP5ocfxPL9n6M0GIbarcxFiqs3wM5j0vwjQ2xW4WnnGSJihEpbxYD2gZAUwXr4xaP86RtXAHBb+Q3Mtk8nEAkQjIYIR8OJv6FIiFA0TCgakpZFpMfxZaFoiFAkTCAaxBtuwzPpTb6xdRsqk4rL106hJ+Sg1hPhLttqtA3rMZ1ygF7P9txFnCmdx49uXMJ3n1pHT95W1tdvZtGSr5H70Y9jW3s9Hf98kt4jh7nsxHF8GTPYaZvTT2S9hyYYJaIUOOU6M8HvtIzM4MgiS0ZmBOJDKGeDUjFwnXgy/HiZnjmV6ZlTOd5dTW1PHZOtZYAUydJrVdy2chLr9tTHWovkVLTjCAgEa2eTKNwgCBSv/AD7WqpYcMxN6xOPUfD5L8qJ8JcIeZkGWru9HKvtTji837VGElXVTikfa4qtfMB6To+U/xSO9CWLa5Rqptunjqs/oXCU+596FENeBxlGPY6Ai0OO/QDM1BRT+vRrRINuGopms+brn+XXf9xHnlqNUqHEGM3F2V1Am6KJ4901zLRXosnJoeBLX8FzYD+nHnucZY6jXNZzEt0LR2ix6PF53axoriHHEcZtUPHadSeJzomiEOTKbjLnFllkyciMwGBRqZGIexEl4+5NX3mdK4pXcry7mn1thxIiyxcIYzVpU2dXmZw4Q91EHHkQlmZE2sxaHO4ASrQErl5GY8dmivbvw/HGa2TecNNgu5O5yNDEhrvrY95RORn6hNiqcpxEISgoj503ydy6fBLbD7dgM0vJ6cm2DmdDOBKlzeGjpbOX5q5equqdhJsrKNUt4SvXXkYoGuaN2g209rZx3VYHfrebbdkLaChZwlUmE+FI3341KgXBthJ0WU1sbdzJTLs0tC0IAr5J03m48GaWdR9hjvskQtUR4naSWQrwajRYvEFWbm+nZUUrhZaCIXosIzMxyCJLRmYEVIqzv8iYDRq+es9l/OaZvgLR24+0MKXYilmvQaNWjCtqNM1WgVFl4ED7Ye6ccjMCAr3+MHl2achvSnEGNQ1OlNlSSdFIezEAD311Fev3NPDS9lpCoQgri5fx8Ir9fPxND50vPIemoBDT3Hlj7pfMhUGon/lsZYlUd9MX9lHf08gkawk61UA7BLtVh0mvRqdR0tTh4fuPv8u1i4q5+4qBQ4sAgWCE1m4vzTEx1dIlPW53+Ab1pzrdLBnyqhUqbim/jp5dO2k9+Cf0UytpzrmcxjYPPd4gkaiYiCBr1UrEXgtl5hKOdh2nqvsklZlSfx74425QqNmSNZ8t9nl867apTM4z8pujj9EU7ERTvZar69cxub2dtnWvUHjPZ8f+psrIjAFZZMnIjMBYIlkAhdmpNhDH6xx88+FdgJTnZdKrMevVmA1qTAZN4rnJoMbc77lJr05JplcqlFyWPYudLXs41FbFW5ukArvKmHD7/meW8uEfvIwys5UsnR2NdRKXX56HTqNCEzueQDhKpbUEpdnMm1fouWV9Gy1/foTib30bXUnpmI5Z5vwiiiL/70+7aXekFlyOG3medNYiIjJ1kHysODqNEn8wwplWN5GoyBvv1LNsdj4+f5jmrl6aO/vEVHxGazIGrYrJBRby7QYKsozk24109fj5+/oqppXaEu0CTY20PflXBK2O3Ps+xbSjPdS2utm4txHo+95JkVmBG0tv5KGjj/B09Yt8e/F/oVL0u3wJAnnFOZwM1FEf6mBB7mWcqNaxtWwJue7XMGx8h9A196C22ZCROVfIIktGZgTGkpMFoNcO9NaaVpKBSqXA4w3h8YVoc/qob/cMsvbg28u06PjE9dMoL7CyonAJO1v28MddLxOsXwTA8jlSWRSzQY3S3oKgjLKsYBFrly1MbCc+nBgKSzkqlbYK9oUOofvIB/D95e80P/hbSr7zPVTWjDEdt8z5IxIVBwgsgOkxcXM0NuNumm3ogss6jZKungBdSWVt/vvRdwa0sxo1TCvJSAipgiwjBXYDFqNm0Chtrk1PWZ5kaBrx9tL80O8RAwHyP38/muwcdJpeQLI9AahvlQb+4kOfWZo8VhRezramXbxxZiM3T16b2HaWVcd3P74Qi0HD32okW8erS1dTtb0efySL3XOMXLXHjWvLZrJuu2PIY5eRSTeyyJKRGYGztW+IkzzTqSzPTIfTxxfvmI1Bl+q5FQpH8PjCuL1B3L5QQoC5vcHY31Dib1OHh7++cYLvf3IxpZZiSgxl1HMGweAiS5PLitn5iKLInqaDqAtOIooCl+cvTNlfPNfl4RePsn2yncXLK9jXfohTxTrm3HEXnc8/S/MffkfR1x9AoUl1tpe5sOk/TBjHYtQgiiJHOo9jVBuYbB06UqlQCPgCYVod3pTl1y4qjgkpI/lZBoy6oet3DkY8miZGo7Q++idCbW3Yrr8R8wLpBiE5lxCgxxtKWR4MRbi1/HqOdZ1g/ZlNsfJSIiBw71VTsBg0NLibOOGoYaqtghJzEQqhATGqpKMyn8DBXlxb3ibzxpvPqt8yMuNBFlkyMkPwwIfn09DuQT+IgeJoSL6b/+BVU5haPHhkSK1SYjMrE8nGw/Hg80fYX91Bh9NHrs3AbPMi6r1nUOXXUmmYRktvG89Uv0iN8zSoIdw4BavWkrINTdLw55HTXSyfLxlSnnBUs/r6TxBsaaFn1w7anvgLeZ/5rDzj8CIinFSjcm5FFgdPdjJrkiRuGtxNuII9LM6bj1IxMMoap6lDiijtPtYGQKZFy3WLS7h6YXFa+tj92iv0Hj6EYfpMsm7vM9TV9hNZH1tbmbI8GIqiV+m4b+aHeOTQ4zxb8zLamVY03nzmVKwGYEN9LIpVIj0XBAExKpJjy+fo5AYWnOjBs38vuQXXpuVYZGRGQhZZMjJDMLU4Y0hhdLYM5nQ9FjItkhDzB6RSItZoIdFeMyp7K6cjW/jpu2eIilHmF8xm5zorot80YBv9SwRl6mzk6LOocZxGRCTnY58g2NGOe89uNPn52G++NS19l5l4jtV2A7BkRi6fvWUmJ5tclOZK58DhzvcAmJ01Y9htrJiTn6h1qFIK/PLzy9ImtD2HD9L18ouo7Hby/+NzCEmTSjRJBsC/+sIyMmNVFxI5hLHyOZOtpXxnyVf57Z6/0mZsIGx08d87f0KZpYTDne9RYMxjRqZkOaFQCERFyNZnsW+KngUnfLi2vA03ySJL5twgm4bIyJwDDINYOoyF+F19/ILj9oYJNU5FFAUcytNoFBo+N+cTPLDyC3xg+TzuGWRWmK5frlhUFCmzluCPBOjwdqJQqym4/z9RZWXR9dIL9OzaMWAbMhcmf3pFElLxUjMVhdaEqD7S+R4qQZkQIEMxp7yv1qHZMHh+1VgItrXR+uifEFQqCr7wnyjNqQWnNUniPzOprJVWPbBGoVVrIVKzBP+R5UR6bLiCbg51HkNE5MbJ1yb6LAjSZIBMnQ2XWUW4ogRfdRXexsa0HJOMzEjIkSwZmXOATpOer5ouVpvt50/t57FvXUlPb5CoK5vAoVVMmt7LJ1euosCUB0g5NIPRvy5jJCpSbCpgD/tp8DSTa8xBZbZQ+J9foeHnP6b1L48iRkWsy1ek5RhkJp5oNNU+weF30uhpZnrmVHQq3RBrSST7YlkMUk5eNBBADIUQIxHESARif6X/4X7Pk18PJ553v/4aUa+X3Ps+ja60bMB+kyNZqctjIqtfvpkogugzEzyxhN99bQlV3SfJN+Ymzn8AASmSlamTEv8dcyaRfbKetrc2YrpJToCXmXhkkSUjM4Hcf/ss6trcaYtkxS84cQsiV6/kIi8G9WQFS1MuMENht6ZeZKNRkaKMQgAa3c0szJ0LgLawkKKvfZPGX/+SticeQwyHsK5aI+doXQT0/4SOxIYK54wwVAigTrIKseiVtP/rHzg3bug76caBbe11Q4r1YGjwpP24+AoEIynL1SrpKDMtWkxqIwtyLxuwrkIhRbLsMZHVVGYlz2ymfdPbGK+7BUElXwJlJhb5DJORmUAWVOawoDJ9hWkjSeVO6lrduJNK9fSPXgyFSqlg+ew8dh5pRUSKZE0ySU7YjZ7mlLa60jKKvv5NGn/zS9r//lfc7+7BfvOtGCqnjf9gZCaM/qdCPB9rVtb0EdeN+7HlBrpYfmAzzq4GlGYLuooKBKUy8R+lEkGp6vd86NdVNhuGWbOH3G98GNtqTJ3RGh8ufG13Hctn5yEIAruOttLaLVlV/PCTi4fcpiAIRGPDhQBdISfmJZfj3PAWvUePyMa7MhOOLLJkZC4iguG+u/kfPPFuIqFeq1Fyy4pJo97Op26cQWGWiWc2nyQaFTGo9dh1NhrcTYiimBKt0pWUUvLt79H+j7/hPXaUxhPHybjqGrLuuAuFduQZkTLnHjEp6uQL+6lxnKLIVJAQG8OhDPq5qXUbszy1AJjmLyD3vk+j1OsnrL8AM8syue/6acyabE9ZHreKaOv20tDuIRSJ8udXJdFot2gHWKIkoxCkAJxBrUev0tHld2BZdhfODW/Rs2uHLLJkJhxZZMnIXET0H7HxBsJMyrfw3x9fOPgKw6CIFbGOxMIeZZYS9rUfotXbTr4xN6WtJieHoq98Hd+pk7Q9/hjOjVIkIPfj92GYWjm2g5GZMCJJoax3WvcRFiPMz5kz8noeD+G//J5ZnkZatHZYcz2r7rrqnAwRC4LAyssG1hacVto3w/f7j7+b8tpIXl0KQUgIzkydjQ5fF5qiYgylJfQeOkiktxel0ZiG3svIDI48u1BG5iLimkG8igqzxnaRUMZEVvwiNNMuDQHGXcEHQ19eQcn3fkDGNWsJtbfR+Iuf0vzIHwg01I+pDzITQ/LQ8fGuagAW580fcb32p58i2tLIAcsU/lZ0PeWrLz/vOXhKhYL7bx98mHGkagmCIBCNpXrZdZkEI0G8ER/Zq1YihsN49u1Nd3dlZFKQRZaMzEWEQafiLw9cyQev6iuLUpg9NpHVP5I10z4NAYEjw4gsAIVGQ84H7qX4ge+gLZuEZ++71P3ge3Q8+0zKMJXM+SP+mUbFKCedtWTr7dh0w3u+uY4cxb1rJ6qiEt7MXoIoKMZ8bqWbypKMlFmPo0UhkBTJko6/2+cga5WUfN/zzq70dVJGZhBkkSUjcxGSPN093z6+SFY86mHSGCmzlFDbU0dvyDugfTQqUtPoTFy09OUVlHznexR88cuoc/NwrHsd5+aNY+qLTHq5akERIE1k8Ef8TMmYPGx7MRzm1CN/BkHA+sGPIArS+XW+o1hxTHo1P/70En59/3J+ff/yxPLhkt4h5vhO6gzDLr8DXU4O+qmV+KpOEGxvn8iuy7zPkUWWjMxFSHJpHJP+7GrIxYnXZEzO35mVNZ2oGOV4VxUAx85085Mn9+H1h3hh22l++uR+thzqm4EoCAKmufMo+uo3UJotdPzrKbwnho+EyQzPiToH9W3uMa1rM2uxGDUsny0VCq9xnAZgiq182PUcb67D19iIdc0VGCYNL8jOF1kZemxmrXSMBumcHynSFteIUVEkUy+VF+ryS6741pVS6R3X1rcnpsMyMsgiS0bmoiTZHTtuUHq2xCuaRJOG+GbF8rKOdElC6df/OsjJRhebDzQlatlVNzgHbEttt1PwhS+CIND80IN4j79HqLODkMNB2N1DxNsrGVqGw/KQ4gj84p8HBiR4j5ZIJJpSwqnGGRNZw0SyQh0ddL36Mmqrlazb7xzTsNy55if/sZT/+9KKESNtfdFayNRKw4VOvwsA08KFKIxGenZsRwyHJ7bDMu9b5NmFMjIXIcnDhWMXWQMjWYWmfGzaDI51nSAc7bvwBEPRRCkfnXrw/emnTCXn3g/T/uTfaPz1L4bfeYqnkqrvuUqV+GtdtYaMK64c07FdrESigxtyjpZwRESljAmLWD5Wli5zyHwsURRp/+eTiMEgZfd/DsEgRYbuXD2ZouyBdS8vFEZr7quMeX5FolEydFYAHAFJZCnUGizLVuB8az2eA/sxLxp+6FFGZizIIktG5iJEnYZIljIWykqeiSYIAnNzZrG5YTtVjlOolArCkSgHajrx+KR6eJohRBaAdfUVIIK//gyEU0uriOF4uZVwUvmVMGJSu2ggAJEIUb+P9n/8DYVWi2XZ8iH3d6nh7+dqPhzRqIjTE0ip8xeJionPtdHTjC/s47LsmUNuo/fwIXoPH0I/bTrZq1fR2SnN1rtxadnYDuACQ5l0I2HSGlEKSpwxkQWQsWo1zrfW49r6tiyyZCYEWWTJyFyEJEeytGONZMWGWv69+WQiURpgbvZsNjds52D7EbTqHMKRKI0dfVPl+wuBqCgmtiUIQlqiT4HmZhp+9iNa//oXUCmxLL583Nu8GPjjS8dG3fbx14+z42grP/jkYopzpKhTJBpFGYtkHeuU8uqGKggthsN0PP1PUCjI+dBHLpgk93SSHK1VCAoytJYUkaXJL0A/tRLv8fcINDWhLSw8X12VuUS58AffZWRkBpBceiQeuThb4nf5wXAUf7BvaHCytRSz2sThzmMMVtqtNxbRAuhy+fn0zzez7p30+mRpCwoo/NJXENRqWv70CKeefTGt279QOVrbPeq2O462ArDzaAuf/NkmPvmzTYQjYuJzPdp1HIWgYHrm4GaxjrfeJNTeRsYVV6EtuDTFRSKSFStHlaG14gr0EIn23SjYrlkLSMn/MjLpRhZZMjIXIXaLbuRGIxC/yweob+uLVCkEBZflzMIT6kVpGZjkfrLZlUheP1rbBcAzm0+Ouz/90VdMYeOs23CrDETWvUjP7p1p38eFzGgnCKzf05DyXKVU0BN0U9fTQLm1DIN6YDmcUHcXXa+8iNJkxn7zrWnp74VI/AYknuuWobUiIuL09yTaGC+bizo3j57dOwm7Bp7vMjLjQRZZMjIXIYIg8OPPLBnRJ2g4kgNgydEpgHnZksN22NSUsrwk14TLE+RTP99MfZs7Ubx3ojgRNPKvgmsIq7S0/f2vhByOCd3fhURklAW/+6MQ4FhXFSLikAWhO599BjEYJOvue1CaLtwE9/ESHzqNv5fxCQBd3r7zSFAosF19DUQiuLZuOfedlLmkkUWWjMxFSr7dSFHOOC6QSddwbyB1CvuUjMkYVQaChuZEw7vXlKdE0HYea8JDN8qsRpS5dSlDMOnCH4zQrbHSMGcNYiCAa8vmtO8DwO0N8st/HuBMa8/Ijc8RwdDY3s8cmyFRGmm2faDI8tXU4N7zDrpJk7EsvbQnFfQ33M3QSjMMu32pESvL0mUodDpcW9+WJmTIyKQJOfFdRuZ9SijSZxfg9aeKLKVCyRRLJQfDB1CYnEQ9NjRqJTdcXsqB0y1oyg+zTXwT2qJoYhZMmxu3c3XJ6rT2MRiW+thSOJPy41txbduC/aZbEAZLFhsHL22v5XidgwefP8KvvnD+hEe+3UBLl+S239zppaLIOqr1KoszqIr5l626LI/fVlWRrbeTY8hOaSdGo7T/6x8AZH/wQwhjzOe7WOifk2WLiawur4PypBF3hU6PeelyXJs3cuZ730ZpNKLQ6hB0OhRaLQqtDoUu9l+rQ9AlLdNqUej0KHRaBK0OpcGAQqs958d6oXKgpoNMs47SPPP57sp5QRZZMjLvU0LhJJEVGGjGWKSZwkEOoCqsIVi9kGAowuQCC1fe1MOulg4UQTPlGaUcb+hCaWtl3ZmNrC5chlo5Ngf6/iTnJPlEBZblK3FueBP33j1YLl+Wln3EiYtMxXmeYZccvVq/p56KosELI/dn4bSchMhy0EwgEmRW1vQBMwZ7du0gUHcG85LL0ZdXpK/jFyh9OVmxSJauT2SRmdrWdu1afFXHCbtchLu6xm5QKgjkf+ZzmBcvGXO/LxXCkSi/f+4IAL++fzk28/tPfMoiS0bmfUqyyOr1hwa8niEWEnHZUVq7UOXXYjXO5Mnj/2Z3616UASvR6mWsWDuTw28fRSzW4cuv5VjXCebmjE4YjIQv0Cc4AqEItquuwbnxLRzr38C8ZGlaLQfithRj9RxLF4FQ32cy2guSAFwxvxC7VUdTh4eankMAzOo3VBhsbaXjX08haDRk3Xl32vp8IRPPyQonJb4DdPkGJrhrsnMo++FPEs/FcJio3080EIj99SMGAkT9PqL+vmXx18WAn6jfj3vvu3S9/iqmRYsvSVuMs6GnN5h4/MeXjvLARxacx96cH2SRJSPzPiUv05B47EsaLvT6QzjcAdq6fQRPzsW2cBcU1XAouo6jHccpNRcTaV9Ajc/Pe2cky4FIVz7q/Fr2th1Mm8hy9QYSj4OhCOrsbMwLF+F+dw+9Rw5jmnNZWvYDEAxLImu4RP5INMq6d+pZNit/wu7Ig6EIGrWCYCg6YuK7Uaei1x/mG/fOQyEIzK3IYk55Jt/d8SR6lZ6KjEmJtlG/n+aHfk/U5yPv0/+BOtM+If2/0Oifk2XRmFEICrq9I0+gEFQqlCbTWU8MEKNRPHvfxVddhaFy2tl3+hLClSSyzoyxHufFzqU9IC8jIzMk5YVWvvoBSagkDxc+8vIx/vuxPby84wxE1Cy1S3lWR7uOY9VY+Nxln8BulCICbx+UikWLXjOZGjtHu47jC/vT0r/ku+B4SZ/MG24GQaDrxefTWgMxHnE41dxDa7d30Dav7arjuS2nefTV99K232SiUZFgOIpZL3mgjZT4HomKlOSYmFZqSyw75TyDK9jDvOxZqBTSPbQoirT97XGCzU1kXHl12odaL2T652QpBAVWjWXQSFa6sF11DQDOjW9N2D4uFpK/w3rN+zOmI4ssGZn3MTNKpcSUAzWdbN7fCMDR06mGmGtKl3BHxU3cXnEjDyz+MhaNmeuXlPbbkkCFcQahaJj9bYfS0rdOV59Yiw/naYuLMS9aTKC+Ds/+fWnZD/RdjAF++c8DsX2G2XygiUhsgsDJRskpvL/dRbrwxQxhM8ySyAoMIrK6e/wcj0cPo2JiOCzOvnbpvV+QOzexzLHuDWk2YcUUsu/54IT0/UJFOUh9TpvOisPnJCqOr07kUOgqpqAtKcVzYD+hrs4J2ceFTihmcNzc2ZtYNtbKFBc7I0rLysrKPOBHwGVVVVWLhmhzD/BT4MtVVVWvxpatAf4AdMSa5QDPVFVVfX/83ZaRkUkHyYakf3+zmk37U32xtBolWVYDV1lXpSwvzjHxw08u5nt/2ZPx0Yy2AAAgAElEQVRYVmmaxV7nDt5u3MGygvHno5xu7rNT6E0azrTfchvud/fQ9dILmObNT8sMuWhSVMzhloYpn3jjBHuOtxMR4er5hYnlduv4jWAHI558bzNJQ5HJ+VkAx89088t/HQTgh59cTDQqpnx+7qCH/e2HMGtMTLWVA+A5fIjO5/+NypZJwefvT/uszAud5ALRcTK0Vk6LdbiDHqxaS9r3KQgCGVddQ9vjj+LcvInsu+5J+z4udH78t73Ut/cZHGvUCgJnUZfzUmI0v04rgJeQ8isHUFlZOQlJSDX0e6kZ+EhVVdWaqqqqNcAu4PGxd1VGRmaiaUq68wT45r3zhmxbkGVMea4XzMzJmklzbyuNnpa09EUASnJMeP2hxPCgJi8fy9LlBJubcO/ZPe79AITDA6Mada1SDklD7GIRj4aolBMzAOCLDdlajZLIig8XNnV4ONno4o099Qi6XpS5Z9h35kxKMWhvyMvvD/6Z3pCXK4pWoBAUBFuaaf3zIwgqFQX3fwmVNWNC+n0hE58tmhzJiie/OwITN2RoXrwYpdmMa+sWqej5+4xkgQWQn2nEP0bft4udEW9rqqqqno1FpYZ6vRaorays/J9+y6vjjysrK3MBbVVVVd04+iojIzMBTC6wpESNkpmUP/SdfnIUBaQL2YLcyzjYcYSnTvybfGMeaqUajUKNSqFCo1CjVqpRK9SoFSrprzLpsUKNRim11So1tDu9ZFq0WEwa6ts9BMPRRGK6/ZZb6XlnF10vv4R50RIE5fiGIsKRkfO74tGuiZowFo9kGfUq1CoFVQ1OPvmzTYnXy2Y60M7egyCIvOWpRmmfiVJhwx8O8NChx2nytLCi8HKuLb2CSG8vTQ/+Vkp0/8zn0JWVTUynL3ASju9Jn2/cK8sZmDjjWYVag3X1GrpffYWed3aRsWrNhO3rQqN/ruSaeYU0d/YSCEZSism/XzhXseMvAI+MpqHNZkClunDGbrOz3x8Gau+X44T3z7GO9jgf+PhifvnkXmoaBt7Zn817ZTRpWVS5iBdOvUq9u4l6d9PIKw1DOKuQSsUaMmMu83qjFrs1Vocv24zvmqtoXfcmvHeA7CuvGNe+xH6/+9nZ5sRQkyiK0vsQuzgYDJq0n0OiKKJoliJn2XYTeq2KULgvaVhh6qbN8C5EVIQ6itDnN6EuPY7GO5N/n36B2p46VpYu5v4lHyXY0cmJ//sVobY2Cu+4jbKbrhl1Py6170ZG7HwxmrSJYyvx58FJCKv8E3q8ljtuwfHG63i2bKLijpvOm53Duf5MXZ6+yN3fv38dGWYtP3hUijhbrAb02gt3yHoi3qsJP9rKykotsLCqqup/RmwMOByDz+w5H2Rnm+nouPSnnb5fjhPeP8d6NsepAqYWWQeIrJwM/Vm9Vw6Hlx5HgP9Z8k16Qz5C0RDhaIhgNEQoEiYUDcX+hwlFUh8HoyHC0XCsbYhqxyk6s5uIettQIiXZNzQ5iQb7crP0V65F2LCJM089DdPnjivfyN/P8b6jw50Yrtt+qJn72nuIxGwegoFw2s+hv7x2nO1HpCHWaCjM5HwLB0/GkqZVQdTlhxGBYPV8oh4b+TmZNKj2UW35FzRCRcYk7p50O601jTT87EeEu7qwrlqD4bpbRt3XS/G74fNKQtXh9CaOTRGQhmMbu9on+HjVmBYswr1nN/Xb9mCYPmMC9zU45+Mz3XuiHYBrFxUT8gfp8AcRYtGtpmYnVtOFaUg6nvdqOHE2pl+lysrKTCBcVVU1mnjrh4B/jmU/MjIy54bku8v/unsOlSU2FGd5453IWVKosGrHd0f4Tm01fz39KHX6zbQJFlSFNrzB1PwwdaYdy8rVuDZvpGfXDqwrx17SJ9QvJ8vlCaTMbmzt9hJP6xlr4ebhiAssAINORWG2kYMnOxEEkanL6qn3+wk1TCHqkewaMvzTqPW0Y8hyUZlbxEem302kvYOmB/+PcFcX9ltvx37zrWnv58XGYDlZtnOQkxUn4+prce/ZTff6dedFZJ0PHnrxKAA5Nn1iWdzk1xsIX7Aia6IYMYOzsrJyNfBRIL+ysvK7lZWVeuABpCFAKisrhcrKyu8CpcAHKisr1/bbxN3A0+nttoyMTDq5fEZu4rFWrUSrVqI+y2H7dIqP6mqR0Kk5qAUdXhyoC0+zp2NgknvmDTchqFR0vfry2MugkFrHEWBvVUfK85pGV+L4wpH0Tv3vcqX6ium1qkRyvTKvlnr/SXDbCbdM7utvUCBUN5OpvbfxuTmfQDz8HvU/+j6h1lZs191A5k23pLWPFyuJnKykc9OiMSMIAs6Aa8L3r588Gf2UqXiPHibQNL7h84uB5Fm6syb3Gd5mxISV0xOkxnGKhw79hUMdR895/84Ho0l83wJs6bf4m0mvi0gWDz8aYv0bxtNBGRmZiSee9wSgGcb1vD9zK7ISw1qRNImP7Ydb2HygCSjg7pyrafc4We96kncdO7gzfCU6Vd+dsNpmw7rmCpwb3hpXNKt/JCs+EeBj11Xyt3VVdDh9RFRutLP30CIUI4qz0pZj8/zW0ynPDToVbtpQlxxHlVdHhtbKHPWNrKc90SbuG6YSonQ88y8cb65D0GjI+8xnsSxZmpZ+XQr0NyOVlinJ0Flw+ideZAHYrr0OX001jrfWkfeJT52TfZ4PPL4QX/rtNgAWT88hJ6MvkpVpkb6zhzuOse3UK4iInHbVMclaikVzaeUB9kc2I5WRkUlhuNIy/fn8bbO4faVUviWchkjW1kPNPP76cbRqJbevmszCaTlYtWbC7cWExCD72g8OWMd27fUgCLi2bxvzfvtHp5o6PGjUCmaWSWatr+2uJZR/EIW+F4fuBHvbBvZjrBh1qfe6Z7xV7Ao+jypPmoz9xbmf5tYlUnmWqUXSUJc/GEEXCTB/21M43lyHOjePku/8jyyw+tG/QHScTH0GzoArrVUDhsJ42VzUubm4d+8i7Jr4IcrzxYm6vlJF/b3kbGYdSnszO3vWoVIomWGvxBf28Wz1yxPaJ5cnQNt5zvOWRZaMjEwKZyOy1CoFRTlSbbfIKGwQhmPzgSaeeOMERr2a//eR+dy8rAy1SoFWrSTSUQRId8ID+pCZiWHaDPynThJsazvr/foC4QFGid3uAJlmHXaLDoMxgqb8EEqLg6jHAqLAK6fX0+0fuf7dqEgOiAlR3mp+EwCzmM2dpXeTb8xFp1Hx8FdXc/8dUl3I1sZ2bmvdgtnZinnJUkq++z9oCwvT059LiP61C+PY9TbCYgRPqHew1dKKoFBgu2YtYjiMc+OGCd/f+cJsUCceZxhT864aglVoyg8TJsSHp93N5+fcR6mlmH3th6h11U9Yn/748jF+8dSBCdv+aLhw51LKyMicF862/EVftGDsw4Ub9zXyj7eqsRjUfP3eeRRl9xXl1WqUiEE9JoWNascpQtEwakXqT5dl2TK8x4/h2rblrB22axpd9JeHHl+I4hwTrd42lDO2Iwo+Iu4MglULyalspEs4wU/2/B8LcuagUqgQEBLDhwICCKCI3cMKgiC9Lj3p9xgawr2g1vBfty3mkGsfu50urixeyZ1Tbk7pk1ajRKEQKPG2cmvbVowRP468cqZ86jNpcb2/FOnLyUo9NzMNkjGrM+DCrDm7AtBjwbJ0OZ0vPo9zy2Yyb7wZhfbSS/5OFrK6pN8Qf9jPlo63ECNKZkZuZFGeNIHl9vIb+b8Dj/Dq6fX857zPTEifWru9E2YePFpkkSUjI5OCRnV2P0rxC1lykemz4c099fxr00msRg3fuHfeACf5hAGpUERd5Ai1rjNMtVWktDEtWITy2X/j3LwR29rrUJlHXy7F4wsOutxqUvHk8X8TFnyEGqYSbpkECPScKidrToCucC3bm985u4MdDA3o58FTzXvxhHrRKjVcWzqE75fbxV0tm1CKUTbZF2BZeQ1LZIE1JIPlZIEUyQJJZBWbJz4CqNBqyVhzJd2vvkzPzh1kXHHlhO/zXJOcLjCpoO/7t7FhG73hXsItFQhma2L5FNtkptmmcMJRQ43jFFNipaDSQTAU4akN1Tg9QaYUWUdeYQKRRZaMjAwAX/vgXBrbPWeV+A6gil3I3thdz91rKkZoncob79Tx782nyDBJAivfbhzQJm4vYY4UAEc43l0zQGQpNBoyb7yJjqeexPHG62dVCDmeRP6BKyt4etPJxHKP6SR17gbmZs9l1548QIom+YMRGvdO5e5rr2T2FHMsCiZK/8SkZ6LY95pI0uvxttKjf2zdR4dYRzDTgyAI3FFx05DRle7XX0EjhlmfvYQD1krWnuVn9X4jLrL65wvaY5EsxzlKfgfIuOJKHOtex7FhPdbVay656GNcyF67qDgRifaHA7zdsB2jykBnaxm96jCdLh9bDjZz64pJ3Fy+lhN7a3jl9Hq+Mv/zaZtM8seXj3GgRpqQk3GeLSNkkSUjIwPAzLLMRKL32aAc48Xi1Z1neH7raWxmLd/80DxybYZB25liuR5KXxYqg5Lj3dXcWn79gHbWlatxrHtdimZdex2qjNHV6ouLrLxMA7k2PW0OH6j9nBbfwag28LnL72X/W1tZMDWbQyc7kfysBYSQniJzwVgOPQWhqxuFK59f3baSYCSUMnsymWBbG66tWwha7ByyTAEGljaSSSV+bvbPycpMimSdK1TWDCzLVuDa+jauLW9fctGs+OSRrKSk9y2NO/CGfdww6Rpe2aPmeJ2Dbz68CwCLUcM1C0uYnTWDI53v8V53NTPtlWnpy5nWPlNRg+78ypxLS0rLyMice5Ku86OdrfXy9lqe33oau0XLtz48f0iBBWDWSyLL54XJGZNocDfhDnoGtFOo1WTeeAtiKET366+OuvtxkaXTKFHFhkqVGR2IRFlbeiWZBgt/+MoqPn3TjBQfoHTNTHP1BrGatCgExZACC6DrpRcgEqFnydVEhVg/L7FoSLoZKifLnpSTdS6x33o7Cr2ezhefI+w6t/ueaMKx9zhejsod9PBm3WaMagNXFq9IfBZx4t+7myZdC8Crp9el7TuVbCejOs/fEfkbKiMjMy56faHE49d31/GH548M+WMpiiIvbD3Ni9trybLq+NaH56f46QyGTqNEqRDw+ELMsktWBhvrtw7a1rp8BeqsbFxb3ybU3TXsdo+e7qLL5ccfK9Wj06ho7pBmmyntkgP73OxZQJ9zuC/QNwsxmoYLQjgSxe0NkWHUDNvOX1+He89utKVliNPnJJZn95sqL5PKUDlZNn1suPAciyyV1Yr9tjuI9vbS+vhj58RCwtUb5PO/2cLWQ81p3e6/N5/kB0+8mxCw8fc4nj6wvWk3/kiA68uuRq/Sp3x3ALSxG5oicwELci6j3t3E4c6Bs4fHQnLB9/7i7lwjiywZGZlxkZzk+tyW0+yr7qDXPzAJXhRFnttymld2niEnQ88DH55PlnV4gQXS7DyTQY3bF2JF4eXYdTY2Nmyl0T3woiGoVGTefAtiOEz3a68Muc1Ol4/fPHOIb/95Nxv2NgKSmBMBQduLwtxNubUMu37o4dNgaPzmqz29UtK91TS8yOp64TkAsu64C62mb6p8ce7Ez4y7mEmIrH7DhRqlGpPaiPMclNbpT8YVV2GYOQvv0cPnxNLhYE0HgWCEJ944kbZtOj0B3ninnrpWNz290k1WoqyWUoEoiuxu2YtGoWZp/sJBt5E81H1N8ZUgCrx2amNa+hdOilyunJOflm2OFVlkycjIjAuLQcPCyuyUZS5PIOW5KIr8e/MpXt9dR26mgW99eH6Ky/xImPVqPN4QWqWGD1beQVSM8rfjTxOODhRzlsuXoc7Nw7V9G6GO1PI40ajIP96s5sk3q4FUp/d4gr2qqAZBgDXFK4bt065jrWOORESjIq7eIO/GiunmDDNc6jtZQ++Rw+grp2GYMZOyPDM2s5bVcwsozb203bLHi2IIkQVSDUOn/9wYkiYjKBTk3fdplCYznc89Q7C1dUL319/CIBSO4vWHhmg9EK8/zHtnunlt1xkefP4IX/vDDr764I7E6/EbhXhOllIpcNJZS6e/m3k5c9CpBv+eB0J9ka19h31EejJp8jalZQg3HBapKLLylweupDD7/N6IyInvMjIy40bdz/bB2RukMKa7RFHknxtr2LC3kXy7gW/cO++sZ/yY9GoaO3oJR6LMsFeyLH8xO1v28EbtBm4uvy6lraBUYr/lVlr//Ee6Xn2ZvPv6Spk0d/aycX/joPuwGDUIOg/KzFYsQjbzsmcP26dOl599VR0snJYDSHk/bm+Int4gPb1BXMl/vcHE8p7eIG5fiORre4F9cJEliiKdzz8LQNbtdyIIAlkZen59//IR3zOZvvygwTzcMnRWGjzN+MI+DOqhRe5EoMrIIOcjH6XlkYfofOFZCj7/xQnblzIpYiSKIj/7xz66XH5+df/yQQRYhPo2D7UtPdS2uKlt6aG1O9Ux3WrUMLcii/fqugmGovR44yJLjO1PweYGqfrC0vxFQ/YrWWT1+sJEnNkorV0c6TzOysLLx3y80ahIVBQTw5bnG1lkycjIjJv+IqvHI/3wRkWRp96qZtP+JgqzjXz9g/OwjpB/NBgmg7ROrz+M1ajhjik3ccJRw/q6zczKmsEka0lKe/OiJXS/9go9u3aQecONaHIlC4bkiIZapUhEsm5aVgaAKr8WQYCp6oWjmk7+0ItHKcwy4uoN0usLDTA17Y9eq8Ji1JCXacBi1GAyaFAqBOZNzR60vff4e/iqqzDOnoO+YsqI/ZFJRTVMJCtD25eXda5FFkjebtrSMjz79xFsa02co+kmmBSt7fGGqG2RZt41tHtQqxTUNvdQ2+qmtrmHxg5Pynul1yqZXmpjUr6FSflmJuVbsJm1CILA1kPNPPHGCZyeAFFR5I13pDJQPZEuDnUeY5KlhIqMSYlt6WL2J7eumMRL22sTie893iAb9zciaHKg9ARHOt8bl8iKC+rzbUIaRxZZMjIy40bRT5B4fCGiosiT66t4+2AzRdkmvn7vXCyGsxdY0DfD0O0NYjVq0Kt0fHT6PfzuwJ94tuZlvrEwNRIgKBTYb7mdlkf+QNfLL5H/mc8CqRdbvUaZEFlWo4aoGEVpayMa0JNnmDxoP2xmLQ536lCowx3AYtRQkGXEYtRgNWr6/hqkxxajGqtRg1o1el8rURTpevF5AOy33THq9WT6GCrxHSBDK5lUOgMuCk3nPm9HEAQyr7+BlkcewrHhTXI//LEJ2Y8/qWSUL8kw+H//ujelnUopUJpnZlKehUkFkqDKzTQM+G7HKYyZBj/++gkpYusJAiIHeqShxGtLr0i5UfneJxZxoLqD+VOzU0TWS9tqARCDBkSfiWrHSYKRIBrl2H4r4hE1WWTJyMhcMiSH/gHcviB/feME2w63UJJr4usfnIdJrx5i7ZGJe934k2YoTbWVM9M+jaNdx6l3N1JiLkpZxzR/AdriYtx7dpN5401oCwpT/JJUSdG3omwjXT4HgkoatlDYB7+wfOejCzhe56C2pYdN+5sAePArq8Z8XMPRe+QQ/tOnMM6bj660bEL2camjGKJ2IUg5WQDOc2hI2h/TvAWobDbcu3eRfdcHJqTcTiDYJ6xO1PfV2yzMMvZFqAosFGWbzkqYlOb15QM+t+U0IEWCT3mrKbUUMytrekr7vEwD119emsjXDAQjeHwhNh9oSrQRe3II6U9T7Tg1YP3h8PhC/GtjDXesmpyIqp/vWYVxLgypJyMjc1HT7vQBfSVw3nq3kW2HWyjLM/ONe8cnsJK36w+lJrrHhxW2Nw0sbyMoFNhvvQNEka6XXwRSbRfUSgWTYzMji3PMNHqk2Yqi1zLk3XumRcfy2fnce/UUVs8t4Bv3zhvXcQ1F1O+n/aknQaEg69bbJ2Qf7wfiPmIdLt+A1+KRrHNt45CMoFRiWb6SqM+He++eCdlHciTrb+uqALh5WRn/++klfPLG6Vwxv4iyPMtZR35USkUiHxEAtR9VYQ0mlZnPzv44CmHw7cVrowZCETb1y48UXTmYvBFc/3qajmefIewc3ezPV3eeYefRVn737OFEJEstR7JkZGQuFQrsRk419bBmXgHr9zQQCEWYXGDhq/dchkE3PoEFST/MwdSI2Qx7JSa1kfe6qgZdz3jZXLRlk/DsfRd/fR0Rsc9u4soFRayYnY8vEMagU9HYLImsqNfMSOlYSoWCj183bRxHNDxdr75MuLMT2/U3oi0qnrD9XOpo1NKFtr7NQyAYSSl+nqGVzgXHebBxSMa6chXdr72Ca+sWrMtXpn37/n7fGQCdNj3lmLRJ0WB1wWkEhcjK3NVYtUPXDo2X7Tp8qovDp1K97AqDBq7f4MTi6cLBGVxbNpN19wewrlg1bBmiePSqvt3D1/4gDVfKkSwZGZlLhg9eNYUv3DaLO1eXYzFqmFpk5WsfmJsWgQWgi0ey+l0wFIKCUksxjoBzUBd4QRDIuv1OAFr+9DBRrzRTat6ULK5ZWIxeq0pYScR9t6Jey3ktxRHu6cG5aQMqmw37Lbeet35cCqiUCuaU2wHwBVOjoDZdzPX9PA4XAqjtWRhmzMR/6iSBpqaRVzhL/MGBNic6TXrO7/gorKDzoMxpIOo3sLZi6bDrDBYlfvhrqynVhrjhxGtYPBH2zjCgvUs699v/9gSNv/wZgeahzVSNg/zOXCg5WRdGL2RkZC5q9FoVC6floFIq+PnnlvLND89P+E6lg+Qhhv7Ec7Hq3YNfoIwzZ2G79jpCra0oXvoHiCLlhdYB7Ro9zZhUZq6dV87i6blp6/vZ4lj/OmIwSOYNN6FQjy35V6YPS2w2a7DfuaNRajCpjXQHHIOtdk6xrloNgGvb22nf9qCRLE16IlnzpmSBEEVddgxBELmh5DrUyrO7sbpj1WQUrm5uqnkVc9CN78ol7Jhronq6jbL//QmmBQvx1VRT/8Pv0fnSC0RDAz2+QuGBxzhYHt75QBZZMjIyaUWrVg6Z0zRWdEMMFwKUmAsBaBxCZAFk3Xk3hukzUZ58jxXdhwb0LxgJSrPMzLl88Kop5+0uOOxy4dy8CZXNhmXFxCTUv9/QquICfaBXVqYuA4ffec4NSftjumweSrOFnl07iYaCad32RIqshdNymLHIidLiIOLMZn7urLPextUVBhp+9XPMQTe7sucz+a6PIiBwrOsEqgwbBZ//IgX3fwml2Uz3Ky9R/4PvEWhsSNlGsk1FHKcnve/jWJFFloyMzAWPdojhQoA8o5R82+btGPBaHEGpJP+znydqzWSF4zD2d9YjJhlUdvmlaEbWMGV0JppoKETbE4/Folg3o1CnZ6j1/Y5GI13mBouC2nQ2QtEwnlDvue4WJ+oc1DRK+WCCSoVl+Qqivb149u9L634GF1npiTK7gx4ahYOIITXBU3POOj3g21fYafrZjwh3dnKkdAlbrLMQIlqKzYWccp0hGJGEkmnefEp/+BMyrryaYFsrTb/9DcG2Pqf8DfsGGgxfPuP8RaOTkUWWjIzMBU88WXawC6Vdl4lCUNDu7Rx2G4LBSPdNH6NLbcF6eActf3yIaFD6Ee/0SQm4WTp7mns+OsLuHpp+80t6jxzGMH0m1tVrzks/LkXikaz+w4UAmTFD0m7/uR8y/MU/D/DTJ/cnnltXSpFL19Ytad2PPxjGpFfzgSsrEsvSFcl6tfZNQmKQUHMFRNToR5lQ/8CH5/PlyhDi478n4naT/cEP85q6EoBfP32QioxJRMUodT19ESulXk/Ohz5C1l33EHY4qP/J/+KtriIUjgyIcC+clsPSWRNj7nq2yCJLRkbmgic+HTteHy0ZpUJJlj6TDt/wIuvpTSd59J1u/lZ0A4GCSXj27aXhZz8m2NZGp68bYNiC0BNFoKGehh//L76aakwLF1PwxS8NO5NK5uwYTqBn6uIi69zOMDxYM/Bc1eTmoa+chq/qREqUZrz4gxF0GiULkuqLpkNk1bsb2dH0DlnaLCLt0gzYeMR5OMRolMx3N6B/7Z8IajWFX/4qtquvSbxe1+qmPOYUf8pVN2D9zLXXk/uJTxL1+2n6zS9xHzyQ8rrFoOZTN47eY2uikS0cZGRkLnjixqHhQZy7AXL0WRz1nqA35MU4RImUt/ZKd8UBpQbHbfcx9fAGerZvpe4H/42wYhoWUwR7uC/RXBRF/CdrqN9wkoBSh2HaNNR5+QiCQMTnw7VlM4G6M1I+jyhKw4+iCNEoYlQEMZq6POVxrE0kIuWXiCKZN9+K/ZbbRlXOR2b0aGM2DkdOdzNvSmr5IpvOBoDjHEeyfvfc4cRjURQTn7l11Wp8VSdwbdtK9l33jHs/Xn8IhzvA9FJbSjmrsx0ujEQj+CJ+/GE/vnAAf9jPm/WbERG5ZdKN/GFbG8Cozt3Ofz+N4631qHNyKfzPL6PJLxjQptxaBsApV+2g27CuWIXKlknzQ7+n67E/kZt7DW2xKPSUooxRib1zhSyyZGRkLnhUMc+bwSJZje0ezErpYtnu7RxQx3AwFCo1uR+/D8OMGbT/4+8UbDrMfUDw5Z/TOGMm2uJiPPv2EepMzfNS5+SiKy2l99gxot4x5PEoFCAI0sVIoQBBgba4BPttd2Cac9nZb09mRMKxWWZvH2jiY2srU147H5Esrz/VUiEQiiREj2n+AhRGIz07tpN12x0IqvFdouvbpDqFxTmmlJJOLf5Gjrja8Uf8+MMB/BE/vnDscdif+jziJxQdaAMBUGQqYG7udKBtVP1x73sXx1vr0eTlU/zAd1CaTIO2M6mN5OizqHXVERWjgxqbGmfOIv8zn6P5D7/j7pZNvG2fj0NtRucUE4nx6qwsFDr9qPo2UcgiS0ZG5oInPtuvtdvLxn2NLJ2Zh0Gnos3h5Xt/2YO5uBfyod3bMSqRpVRIQsey+HIMldN45a8/ROsJMsNvwfveMbzvHUPQarEsXU7BFStxNLfTe+QwvUeP4n53DwqjEfttd2BZuknbrSYAACAASURBVBxBrU4STQKCQgBBAQoBQVCkCiuZc87Cyhz+uaFm0CGyzFgkq/scGpI2d6aKc68/nBBZCrUGy9LlODe8iefQAcwLFo1rX70+ye7AbEhKSFf7efjon4mKA29Y4miUGvRKLQa1nky9Db1Sh06lRafUoVfFHqt0zMmaiXKUM3EDjQ20/uUxBI2G/M9/cUiBBVKZnMkZZexu2UtLb9uQtSVNc+dhvuVOeOlZbmqXTEhpgrqdfW2Ms+dQ+OWvjqqPE4EssmRkZC544iLrdHMPp5t7+Mdb1cyclMmxWimXqtepRZsP7SPkZcWJ17QDEE1GNs5SU54xhevnfQ7/yZOI0Qi60jIUOh32bDPRDjfWFasQw2FCjm5UGRmyh9VFgs2sRa9VkmUdGNEwqY2oFapzOlzY2ZNa4sfrD5OZZJBuXbUa54Y3cW3dMm6RFZ9ZGM9Lu+eKCg64dtMoRrmieAXTbFPQqWLCSalDr9KiVWpRKs5uuO3X9y9PFOMejKjfT/MffocY8JP/uS+gLSwcdnvdPQHKrZPY3bKXU87aYQt4G668lt/t81Ho78AecmExqFlQkYkgKAh1daIpLBpy3XOBLLJkZGQueFSDlMiICyyAbL2dHqBjhBmGcZIvCB2+LkREcg3ZCIKAfsqUIdcTVCo02TlDvi5zYaJSKgYdahYEAZsu45wOF7pi/k0luSbq2zw4PAGKcvqiOtqCQnTlFXjfO0aoswN1VvZQmxqReHHoeI7S2sXFvPvOM6j8Km4ouxrDEPmLZ4vNPHxh687n/02oowPb2usxL1w8aJvvfWIhP3xiLwChSJRyWxkAp1xnWFW0bMhtR6MibTp7IicrL9PAjR+Xapo6/E4CkfPrlyVPYZGRkbngSTYHLc0zk2lJ/VGPBLWoFWrah/DKivYzm0yOZLV62wHINcji6VJFpVQQGsSwEiBTa8MT6k14Mk0k7U4fT286CcCUQikfrMM5sHi1ddVqEEVc27ed1fbDkSjfeGgHz759CuiLZMVFVoevi1ZvOzPt09ImsEbCW3UC56aNaPILsN82dLHzsjwLtywvAyASiZKjz8KkNnLKeWbY7fc3klUn1VN89OiTPHjw0TH3PR3IIktGRuaCJznyVJxt4sefvpzbVkxKLItEIMeQRZuvc1D37v7JxskiKy7Mcg1jjxjIXNioh4hkwcQnv4uiSCRmfLu/qu8mYGqJtN+uHv+AdcwLF6PQ6+nZsQ0xMtB6Yii6XH66egK8vluyPgj0E1lneuoBmJIxeQxHcvZEAwHanngMBIHc+z414hB7/GYqFIkiCALlGZNwBJzD+pjFy+fMKbezcFoO/3HLTAB8YT91PQ2JvLvzhSyyZGRkLniSk8bz7Qa0GiU3LC3lkzdMJ8uqIxSW7nyDkSCuYM+A9eMJwHGSy+q09kqRrBxZZF2yqFRDR7IMSjMAjc6hKwaMh58+uZ/P/OJtANxeKVp2/ZIScm1Sjthg/VJotZiXLCXscNB79Mio9+XqTY3G9eVkSZf62pjv1Ggmh6SDzheeSwwT6ieXj9hepUy1aplsLQXg9DDRrHiU2mLQ8IXbZlGYZQSkYxURKc8oG8cRjB9ZZMnIyFxUFGZL+SsqpYIVc/LRa1VEolFyY+V1WjwDp5N7+oms5MhYo6cZrVJzXkvqyEwsKqUwpMdac7O0/JntRydk3yebXAD4AmEa2j0AXL2wOHEORoYoZJwoGr317VHvy+kJpDzvn5NV21OPSqGiyDTQmyrd+OvrcG58C3VuLvZbbhvVOsq4VUtMeJZbpWj1SdeZIdeJB677+/eedEoeWxUZkzifyCJLRkbmoiI7Q5fyXMq3ESkxS7OI6twNA9Zpc3hTnseHFIORIK297RSZCgb14pG5NBhuuNCikab2OYMTm/z+2q46jtZ2U5Zn/v/tnXd8XGeVv587Vb3L3Zb7ke04duKSXkgnOBBKQiDUwAKhLD2b3y4LgVDyo+yyC2zYXUroLEngAwTYJIQkhPQ4ieP6uksusi3b6m3q/nHvSKPRyJYtjabc8/wjzS0z7/ede9859z3nPYeqssDgrE10lHYVzWkgOKeBnk0biXZ3j+kz2ruGjKxwJIpptt1swYCXgWiIA90tzCmfic+T2TVv8ViMIz/9EcTjTLn5HXgCY1uJO1jZwXGvzi6fQdAbwLTtGPWcxExWaoqUne17sLAGZ8OyhY4qiqLkFakrmXxei2g0RoNjZCXiTpJpPjz8R+q482N0oLuFOHFml594SbmS3/h9HqKx+IgFEABFcXtm1Ar28cnvPEl/KH3izfGSiJO67OxZWJY1NJM1ygwbQPk550I0Stf658f0Ge3dQ+7C93/9cTbtOkZlaYAZtaU0d+4nFo8xtyLzrsKOJx6nf/duyteeQ+nSZWM+b2gmy+4Tn8dHY/UijvQeHbU2aSImKzkEIByL0NS1j5ll0yn2ZTcZqRpZiqLkFaklQXxeD3GgzF9OdbCKvZ37RgS/J4KL33GNMLOulLMX2/FX+7oOAKiRVeAMxvqkiX+yIsXE42AF+mnrGmC9yUxsVoIpTizWQLyfwMKX2BV4lN5wb9pjy9fY6Q66nn1mTO/dluIuBFgwsxKPx2JPZyIeK7MzO5HOTo7efx+eoiLqb7zplM5NzGS1dQ0tBlhW1wjA5mPb0p6T8LYmG1lNnfuIxCKDNRCziRpZiqLkBe9/7bK0hV+H3C5xGipm0xXqHrFSrKM7hMeyuHjFDO587zmUFdsZsNXIcge+ExQYD4ctCAexAnYqhe//YSuf+d6z/Oavu9l3pDvtatXxUF7iJxaPce/ue/HWHKbD18T9Ox9Ie6y/ppbiRYvp27GdcNuJE6Zu3nOcZ7eMjEesKrNddXs77BneeRmeyTp63/8Q6+2h9vo34qs6tZV9ie/pN08M1SxcVntiIyvx/SR7+3e27wYmbxXliTipY1ZEpgFfBFYYY9KmnxWRG4GvAB81xjyQtP1c4EogBrwKeLcxZmTAhKIoykk4Z+nUtNsTiUrD0RhzK2bzcutGmrr2UVs8NMC3dw9QUeof9rQLtpHl9/iYpjmyCppEgfHUlXxtXQO8vLOV+IwirJJOIM5Zi+rZtOc4v39qL79/ai9TqotZJfWslinMnVZ+SuWREqkbkqkoDfDA7ofY0bGTaHsdJWVRnml5gXOmrWJx9cgVeOVrz6Vvx3a6n3+W6quuGfWzfvrw9rTby4r9xONxdnc2URWspNpJWZEJercbOp96kuDsOVS96rJTPt+XpkRPVbCSmWXT2dG+m4FoiKB3eHxXwgWcfG/nStA7jG0m60Lgt0DaK0tE5gGtwL6U7RXAp40xdxpjvgS8Dzie5i0URVFOm+QA4rkVs4Ghp/YEXX1hykuGD87hWISDPYeZUTb9lMuIKPlFom5hIqUBQGdPiE9+50la2/uJDRRjeeL8y8dX85E3nsm//f2FfOB1y1jTOIWO7hB/eqaZO3/0Arfd/TS/fGQHO/d3pI3vSiUUHjKyioM+rryglvt238+DTX+htqiG0K4V1HfbLsH/3vhjthwzI96jbPVq8HjofO7ZE35WWXH6OZPVjVM41n+crlB3RmexYqEQh3/0A7Asprz9nVjeU7+nku3XWNKqy2W1jURiEba37Rz5uU4XJ4ysaCzKro69TCuZQnlg9PqIk8VJZ7KMMfeJyKUn2L8H2CMin0vZdS3QLSKfAMqALcaY+8bTWEVRlFSSExjOLp+FhcXezqFnvng8TigcHVzGnqCl5xDReFRdhS6gJGj/1PUODAW1JycBjYfsOKnj/e1UBSspCvhYu2Qqa5dMJRSOsmnPcdabI7y88ygPPb+Ph57fR1VZgFWLp7BK6lk8u2pYgtsEIWfm7OzGWhrPbud3u35J9FCUmWXTeUfjTXz2r1vxh6p5W+MN/HL7b/jPV+7hn8/9FHXFtYPv4SuvoGTpMno3bSR0+BCBqdPSavQnzQLd9YHzqC4LECgOEg9HePLAZgAWpZkpGw+xUIiel1+if18z/Tt3ED58mKorrx5TTqx0DISHjOC+UITSItutv7RmMQ81Pcr2tl0sr1s6vA0p7sJ93QcIRUM5MYsFma1d2ACcA7wXiAKPisgxY8yjJzqpuroEny93nirr68uz3YRJwS06wT1a3aKzrNSeoaqsLGFGfRlzKmfQ3L2fyuogAV+ASDRGPA6lJf5hffJK5zEAlk5fcNK+cktfFqrOuho7QWWgaOgaKEqa2YwP2GlBooH+tH0wc0YVV18wn3AkyoYdR3lyw0Ge3dzCIy/u55EX91NZFuDcM6Zz/pkzOHNh3dDsqqcHvGH2lP+erTs7KfEX895VN3H+nNVYWMBWPF4Pr11xGTVV5fz7Mz/g/t2/4/aLP4QvaXY1fvml7Ni0kdjml6k/44a0GhtmVLKtuZ1brlvGskXD3d+7d9jus/MXrqS+vJze/fsZONJKPBazM8o7f+Ox2PBtsRjxqP039ZhwRwdH/vIo0Z6hoP2qlSto/Lt34g2euJbhaPh3Dzm7ikuLqK+xS/9UVi/Fu8FLU3fziO/nSJe9orKsNEh9fTlPHzsIwNlzlp7y9ZyJ6z+TRlYn8JIxJgwgIk8DlwAnNLLa2tKvssgG9fXltLZ2ZbsZGcctOsE9Wt2iEyASsZ9+D7d24SfOwsoFNHUc4KmdG1hW20hfYvYiFh/WJ1ta7ODYampP2Fdu6ctC1hl3rpGWw13MrC6mvr6cAy1DlQEW1c+kia28sn87i4rlhO/VUFdCw+ULufHS+ZjmdtabI7y4vZUHn2niwWeaKC3ycf1F87l81SyaWzrxTd9Nv9XJOdNWcf3Ca6kIlHPsaA9gJ8Xt6w/T2trF4mLhjNpGXjm8lS//5dvccsbbBuOPQnOFuM/HoUf/SvBVV6eNC+tyVhY2zqwY/B7r68s5fKSDV1q2Uh2sgkMhNt/zXdof+fNQFs9x4Ckro+badZScsRxfVTWBKVM43hkCTq8O5JzaoXqK+w+240kqKdRQPos97fvY13KUIt+QEdfWZvdlX5/djxsObAVgimf6KV3P47n+T2ScnZaRJSI1QMQYM7J+xRCPAu9Iet0A/P50Pk9RFGU0fJ6h1YUAZ9Qu4ZHmv7Lp6DaW1TYOumySC8eCHfTusTxML0vvflEKh5Kike7CrqQqAHNK53DYW8Rzh15kSc1iAt4Afo8Pv8ePz+Mb8b/X48Xn9bBsXg3L5tXwtquEHfvbecG08thLB/jlIzsIR2Lc++x6gkubKLJKuUneQMDrH9Yur9cavG4ty+Ldy27me5t+wqZj2/jvjT/mQyveg2VZ/OJv+6gJzqCxpZmtT6xn6cWrR2gMhRMldIZ7gvZ1HaAv1Mu63T723PNxiMfxT51GxfkXYHm9WB4PeLxYHgs8HiyP1/nrAa8Hy/LYr70e2yfn8QyeF2xowFtSOjFfElBbWcS68+fywFN7R1RpWFA5j90dTfz7Hx/n49dejt/xeA2lcLDjsXa276GuqCajAf6nwlhWF14CvB2YLiKfAb4B3I4dxH6XiFjAP2EbUW8WkbAx5kFjzDYR+YmIfBUIAy3ALzMlRFEUd+LzDa0uBFhQOZdiXxGbj20lHn8dYWcWw58UhhCNRTnQfZAZpdPwZzj7tZJ9EkZW8g/3/tahBLWhEFzVeCm/2/2/fGfD90/6fh7LM8L4Cnj8+Kp81J8d5/CWBn699WGCywyWBedUXjbCwALwejzDyuoU+YJcUPZaevt/zdbj23nxyCusmrqCjbuPU1EpSE8zkZ/+J0fjPVQuX463pBgrEMTyeNjouNoSdQoTbGvZzGsf72B6Syv++nqqLr+KyosvGXMW9skmUdPxSHsfydFXC6vm8XDzY+zs3MVzW5dzwfLpQNLqQo/F7o4m+iL9rJl61mQ3e1TGEvj+OPB4yubbkvbHsVM8fDHNuf8x3gYqiqKciKGZLNvI8nq8NNYs5qUjr3Co9whE7BVGyT8+B3sOE45FmKNB766gvtL+4W5t7xvctvvgkCMmEotzxZxLKPYV0xPuJRILE45FCMciSf+n2xYhEg0TjobpC/cxEB0g4olSdIadfy0e8RNqWsLyq85I2y6vxxqWuyscifGt+zZhBWdRfOZefr3zAZbXLcHv89BUMp3fTr2Ia488zfGf/HBwqb4VCOBffhaz2kvp8RYRMVvpjtjuukh0gCk//w2lnSGCy5Yy6/0fxltSkqYluUMiWeuR433Dti+uXkg86rVziyUlXY0nZXzffMx2FSZya+UC+ginKEpeM5gDKenH6ozaRl468gqbjm5lcXAVMHz11Z6Oycl+reQG9Ykf7rahH+4BJ53D0rnVvP6i+Xg9Xi6edd64P+u7T/yRDV3PEeupILJvMfFQyWAKiVR8XmvYTFZnj20cxQdKCLU00D5jD8+0rB90dW8rn8fhYC3vnNZOPf3E+noJtbQQWv8siXD4g//2x2GfUWTBzhVTefUHP3laaRUmm6nVthGYWm804PUT66jHW3OIlr5DwFwgyV3osdh8bBt+jy9tvrFsoUaWoih5TSIZaSQ6PK8O2Fmi5zmuA3/STNZklRhRcoOg30tx0MfWpjbue2wXt96wklAkSl1lEZ+6aWJdS3P8y3juleFFzEczsrwez7AC0R09QwHjkUNzKZ7ZzGP7n8TnvXhwe1uggr3LVnDmBXaKgng8TvNzG3ji3ocoCnh51XkL8RTZn/9i1xb+7NvP9WtuzAsDC+yM+MVB7zCDOIG3YybUHGLT8U189vsWb7p0waCR1R/v4mDvIZbWCgFv7rhCtayOoih5Tbq6dOWBMmaXz2R3RxO9A/ZgHXBisuLxONvbdlHiK2ZqSf3kN1jJCgl3caJQcygcG5E7bSJIV7pntM+pLg/S1hWip9+OFetMMrKIBKmMNnC49wjeso5h5x0+PjTLY1kWNCzgL3WrCV/2Wmqvex3VV17N8TXCb6oOUTZ1Jmum5U6M0smwLIvaiiKOJ9UvTBDrqiMes+gPHGJ/aw/Pbz0y6C48GN0BwPLakaW3sokaWYqi5DUJI6t3IMJPHjTscZbmL6lZTDQepbnX/lFNuFwOdLfQPtDB0lrBY+kQ6BaSs6/br6MjgsQngmh0ZGqE0WayFs2uJBaPs/9IN/tbu/nrhoPD9h/cYZeG6i3Z62yJ4Zuxi808xJef/hbPH3rJ/kzH0PA6CVF3te/lPzfeg2VZ3LD4tXl3nRcHffQPREfWjYz6iHVXYZV2gjdMLB4fDHw/GNmJ1/KyOoeC3kGNLEVR8pyEu/D3T+7l0ZcO8IM/2MGvS2oWAdDUYydiTBhZidIlufbEq2SWvqT0DbFYnFAkNji7OZEsmFkJDK2Sg5FpFRIUB+yInXA0xme//xwv7zw6bH+soxbCQdp9e8GKMXflYfyzdhAubeFA3z7u2fILHmn+62AJGq/X4tmW9fzri3fTG+7jfatvZnH1wgnXmGmKgz7iDM8ADxAnTqyjHssCX/1+whE70TD+fjpirSysmkeJvzjte2YLNbIURclrEjNZiTIpiQD4eZVzCXj8HOjfC0DAMbJ2O/FYC6vnT3JLlVwhFEmfU2oiOHNBLXe8ew3vWTeUgCBd4WMYMvxTC1dfdnZi1auH8NHpWL4wgRlNHAtuIh4K0r/hIvpfuZAgpfx65wNsat8AQDfH+Om2eyn2FfGxsz/AZfPPn3B9k0G6WpNg50+NHJlNPOLDN30Poag9m+WttI3TXFpVmECNLEVR8hp/yg9YwsXg9/hYVL2AjuhxrEAffp+HeDzO3o5mqoNVVAUrs9FcJQdodYKqM+EuBJgztZwZtSdP0jlYdzPFyLr5ysW85Qp7JjZ61Da4vDMNkXiUUNMS4gOlxPvLaAxfQ7GvmMdaHwJPhK2xx4jFY7x72Vtzpnbf6VDkzPD1DUSIRGM8tallaNYq6idyuAHLH+JYYBuxWBxvVStgryrONdTIUhQlr0mdJUheZdjouAw9FccI+Lwc62+jK9zNvMo5k9pGJbf44Ff/ApARd2GCkiIfH3njcm69Pn2OLEg/k3XWojosy6Km3F4hGO8rJ9pVBXGLK+dcyjWydvDYSG8Jl82+kHA8RHDZ03TFj7Jm6lksrT1xaaBcJ3km6+Hn9/G9B7bys4e3E43Z/RQ5NJd4xEdb8RYeeG4HnsqjlHoqmZKDC1nUyFIUJa9JxGQlSM47tKRmMQCeyqP4fR72JvJjVaiR5TYWzKgYsS2YoZmsBGctqmdN45RR96czsm55jR0reNbiusFtIbOa+gPXcf3Ca3nTpQv5r09fCsALppW1U9bgtXx4invw4OG6+ddkQMnkMmhkDUTY32rXJnxqU8tQucWo3za0vCGOz/4DljdKVWxW2pqO2UaNLEVR8prUmoTJeYemlUwhSCneimN4vbCtbSeg+bHcyMdvXMmcKWXDtvl82f0JTLi6u3rt1A0rF9ZRWmSX3/FYFm91XIbEfPitodxbPq9ncCXhN362DV/zGqIdtZxXfD21xdWTqCAzJPqgozfEliY7t30kZdVmpGU+kWPTsDwx4lEv6+TCSW/nWFAjS1GUvCb1hzJ5MLYsi1rmYPnDPHb0T7xw+CWqg1U0VMye7GYqWaakyMfM+hQja5SA9Mkice02H7brKNZWDE9iesXqoet014HOYfs+ddNKwM6Z1d5SScisYWpgViabO2mUldhG1nrTSkd3aMT+MxfUQtxDeNcKBraspb7lWs6cmTtZ3pNRI0tRlLwmNfA9FI4O5s4BmBE7k3goyNaujYRjEV499/K8yxukTAyps55nL85uDE+iPTsP2MlG580oH/XY0qLhBVoWza6irnK4UZaY3cp3youHjCyAyrKhDO4XLp/Ox25Y4byyiHXXcMtVKye7iWNGRxpFUfKa1GX4cYbq0gF4I6X0b7yQV89cxwfOfBfnz1iL4k5WiW1UXX1uA9/8+wtZODO7K0wTDwiJcjp1laPnePrcu9YMe+2xLFYuqhu+rUCMrMRMFkBFiZ8Lzpg++Hpbc9uwY79wy1oapo1unGYbrV2oKEpekzqTBfbS7+KgPbyFIjGI+jl36hrqqnIrUaEyuSyfX8vn3rWG5Y1T6WzvPfkJGSZ1Zq2qbGTNvRl1pbR1DaS9dhPJTBMUykxWZWlw8P8Llk9nem3J4OuuvvCwY6fW5PY9rUaWoih5TeoPFQzP7p3IGh0cpbSJ4i4appVnpGbh6ZB67VaWBUcc84X3rLWnZ9OQurK2UIysZGOzYVr54AMTwBsuGp5E2J/BNBwTgRpZiqLkNemNrCF3Yb9jcBUFdLhTcovk0juQvpC0x7JgFNspNWWBN8uB/BOFZVlce24Dew91ctaiOvYe6hrcd8VqO7j/q7eeNyKJay6io46iKHlNspFVWRqgoyfE0c4+FmLH2/SHovi8VlpjTFGyid/nZVZ9Gftbu0/r/NS0UJ4czBN1urzp0qHVgslu0YRheaL4tVxCRx1FUfKaZBfJkrl2jqCDR3sGt/WFojqLpeQsN7zq9FMPpBpVXm/hGFnJFOWxq1+NLEVR8ppkl0litdgzmw/z0X9/gubDXfQNRPJ6kFYKm8pSO/4ocBozrSPchQUSk5VKUTB/H5Lyt+WKoigpzHKSTR7t6Afg5w9vpz8UobYiP1wLivuYM7Wc9123lPmnkU4i1Tt4OoZaPpDPD0lqZCmKkvfcfvPZNB/uYtGsSrwea7B+4fb9dpLHkmD+DtJK4XPusmmndd6CFMNsak3JKEfmN9nOzD8e1MhSFCXvWTy7isWzqwAoDvroTsmlU51SrkRRCoGFMyuZWV/KAaeIcsL1WIh86e/OycvYyvw1DxVFUdKQamAB1JSPzD+kKIXAzLpSwI7HSo3RKiSm15ZSnYf3sRpZiqIUJAtnDblSanQmSylQEsHuqYXSldxAvxVFUQqKT920kilVxbx33dLBbTqTpRQqXo/9M56uvJSSffLPwakoinICls6t4a4PnDdsW5UaWUqh4ngIU0vsKLmBmr6KohQsiUBgdRcqhUo0apeWyecVeIWMzmQpilKw3HHLWtq6+gt61ZXibiJRO12JGlm5iRpZiqIULJWlATWwlIImFLaLoauRlZvot6IoiqIoeUqF8xAxrbYwE5HmOzqTpSiKoih5yjXnzKGs2M9rzpub7aYoaVAjS1EURVHylOm1pdzwqoXZboYyCuouVBRFURRFyQBqZCmKoiiKomQANbIURVEURVEywEljskRkGvBFYIUxZs0ox9wIfAX4qDHmgaTte4G9zssDxpibx9leRVEURVGUvGAsge8XAr8FVqbbKSLzgFZgX5rd9xhj7jjt1imKoiiKouQpJ3UXGmPuA7pOsH+PMebRUXZfLCK3icidInL+6TZSURRFURQl38h0CofbjTHPiUgJ8KKIrDPG7DzRCdXVJfh83gw3a+zU15dnuwmTglt0gnu0ukXnZOCWvnSLTnCPVrfonAgy0VcZNbKMMc85f3tF5GXgAuCERlZbW28mm3RK1NeX09o66iReweAWneAerW7RORm4pS/dohPco9UtOieC8fTViYyz01pdKCI1IlJxkmMuF5FrkjYtBHadzucpiqIoiqLkG2NZXXgJ8HZguoh8BvgGcDtwHLhLRCzgn4AG4M0iEjbGPAgcAe4QkbOBGcD9xpi/ZUiHoiiKoihKTnFSI8sY8zjweMrm25L2x7FTPHwx5byNwBsnoI2KoiiKoih5hxWPx7PdBkVRFEVRlIJDM74riqIoiqJkADWyFEVRFEVRMoAaWYqiKIqiKBlAjSxFURRFUZQMoEaWoiiKoihKBlAjS1EURVEUJQOokaUoiqIoipIB1MhSFEVRFEXJAGpkuQQROVNEqrPdjkwjIpeIyMJst2MycItOZeLQcaDwcIvOfMW1Gd9FZA7wbeBrxpgnst2eTCEijcAlwKWAH7jbGPOIiFhOSaSCQERWYZd7igKfNMa0FJrGBCIyF7gWuALYAfzKx8nhIAAAC49JREFUGLO+UPVmEh0HCuua0XFAx4FUsn2Pu3kmazZQB3xYRIqy3ZhMICIB4OPAfuBm4AVgIQzWnCwInCLmnwO+bYx5K1AsIsWFpDGBiFwLfBL4E/APgAWsgcL6TicRHQcKBB0HdBwYhaze464yskSkwvnrBzqANwErgddls10TjYhUOv/OABqAVmNMzPnfIyLnOMd5s9TEieYo8ARwREQ+BrwD+K6I3AogInl/nYtIufPvMeCIMWaPMWYH9qxEe/Zaln/oOKDjQL6i48DYyKV73BXuQhE5H7gImAv8BHjeGBN29t0KvAu4zhhzJFttnAhSdRpjnhKRzwH12Nb808Bh4PvALGPMwWy1dTyIyGXAOmPMJ5K2vRuoBb5rjOkWkXXAt4BVxpjjWWrquBGRM4G3YWv7jjHmRREpNcb0OPs/gf002wL0GmNC2WttbqPjgI4DWWrquNFxYGzk4j2e95b9yRCRecD1wH8DrwDvBN6X2G+MuRsIA28WkRoRqc9KQ8dJGp3vEZFbjDGfB+4BHjHG3GWM+SHwY2BV1ho7Dpzp3quBq0TkzUm7fgX8yBjT7bx+BPgz9qCUd4hIUEQ+jx1v8TWgG/gMgDGmR0Qs59CZxpitwDrgg1lpbB6g44COA5PcxAlBx4Gxk6v3eMEbWcB5wHznKeaHwJPAJSJyRtIxXwG+CdwOlEx+EyeEVJ2PA9c4F95rgNeISLGIvAmIY8dl5CNTgMeArwMfEhGfs70XOJb0vb4Rewp936S3cGKwgFbgL8aYVuCLQLmIVIEdd+EEdJaKyKeBdwN5OSMxSeg4oONAPqLjwNjJyXvcDUbWk0CdiKw0xvQDz2HfcMthMCi0CrgT+KwxpilrLR0f6XQeBJYBXwXagO8CAeADxpiWrLV0HBhjmo0xfwIeBo5j3yyJYE8v8AkRuRvoA97v9EU+EgZ+B2xwXpcCTxhj2pOewNYBr8b+TtcZY341+c3MXZKe8qGAx4Ex6CyIcSBZZ6GPA0ladRw4ASnxhDl5jxeMkSUiq0VkWtLrxEXai/209h4AY8w27Ccby3kdAu4zxtyRDzfiKer0ADXGmD7gFuDDxpifG2MGJrnZp0w6nSk/Jq3Y7o/XiEitiHgc3/uXgc8bY+7PB52QVqvHGBN1fkgSQZOzsFeHga25HNgCXGOM+ZLzHbseEVkjIq+Bwaf8xBhXaOPAqejM53EgVWchjwMjvlMdB0YiIpeJyC8AjDHRXL/HfSc/JLdxpsH/EZgDvN/Z5jXGRJ1DwsD/Ap90YhN+gP3kE068Rz7chOPQ2QdgjOmd/FafOifT6UyT9xpjQiLyJ+BG4G/Y0+g/M8bszE7LT50xau13BoTZQEhE/gOYDjxkjHksKw3PQUSkDjtG5yzgUREpxb5OYs4hhTIOnK7OfBsHRtMZd/YX0jhwwu9UxwEbsfOC/T9gqvN6uTFmY67f43k9kyUi52EHuT1gjLka6HWmDxM34p3AF4DN2DffGufifMEYc2+Wmn3KqM5BnV8AvoS9JB1gMfay5W8ZY36WhSafNmPU+mXs6e8yIBFv8bIx5vUmT1eEZZBLgCZjzKeAZmebF0BEvkgB3B8OqrOAxgGHk2l1/TggIjdhr558EngL9mKGPc4+n9iLA3Ly2s/LFA7iLF0VkVnA3wE/w34SmIbtc30e+A22n/4uY0yHc54fsEyeLG9VnSfVWQd054N7J8HpaHWeZD+InaW7LUtNzzlEpMQY0ysiJdhxFvcCgr18ezp2moKvAv8MfCWP7w/VOVznZ8j/ceCUtbpxHBCRMmOn4pgPXGGM+S9n+7eBx4wx94nIIuzUDF/NxWs/r4wsEVmGHeRXD/yPMeZ5EXkdcA7wDeygzhuAjwBvN8YkLF1P0pRizqM6T6oz2U2aF4xDq5bISGGUvvw8domRtxhjdorIWuwfrmuNMZud8wrh/lCdQzoLZRwYi1ZXjQPp+snZ7jHGxETkOqDTGPN4ynk5d+3njbvQsVbfhv3kvxv4BxG5wRjzW+C/jDHHnM59HDtHRtA5z8q1Tj8RqnNMOvNtYB2PVtcMrGMhTV/eLiKvB+7Gzui8CMAY8xx2zqRq57x8vz9U50id+T4OnIpW14wDafrp0yJyM0DStb0EaHSO9zh/c/LazxsjC7sUxEpjzH5jzH9g+2avEJGzjDF7RWSlc9waoAY4BHlZx0l1FpZOcJfWTJPal3/DngUIYsdkfF5EqkXkGmwX7HbIy75UnYWlE9yldTyk9tPTwEUiclbSMb8HZjizmTHI3X7KJyNrD9Apdtp8sFcRxLD912AnpPseUAncaozJ1zpOqtOmUHSCu7RmmtS+fBC7LxuNMXcCv8YODp4HfMTkb4kc1VlYOsFdWsdDuvEyztB4CTAfeDIfZjNzzsgSO3tt8utEsrEwcAB4tYj4jF1CwI8dLAjw/4GvGWN+mg9BgaqzsHSCu7RmmlPoyy3YK7GWAhhj7gLuMMbcnQ/GquosLJ3gLq3j4TTGywVJh/+vMeahyWnp+MgZI0tEykTkrcA3ReSbInIV2MnGnL/NwHpsa/ajzmnbgCZn/05jjJn8lp8aqrOwdIK7tGaa8falc0zWVxSdDNVZWDrBXVrHwzj66UDiPfJhBitBThhZYhf7vA0YAD6Ebd3XJu2/TUS+gb28/dvA2SLyNeAY9nL3vEB1Du4vCJ3gLq2Zxi19qToH9xeETnCX1vHgxn7KpYzvbwfuN8a0iEg7MN+ZTuzCznL7L2ZoWfsHgagZqrSeT6jOwtIJ7tKaadzSl6qzsHSCu7SOB1f1U1byZIldTuRK4JfGmE5n25eBM4BO7GnBHueYOxO+V8nBHBgnQnUWlk5wl9ZM45a+VJ2FpRPcpXU8aD9lYSZL7ErYt2GvoOgDfgJgjPlHsZeyv8EY81kZKgJa5ZyXV52uOgtLJ7hLa6ZxS1+qzsLSCe7SOh60n2yyEZMVwE64+GfgPMfSTbAEeKfz/3nY1u5WGJaELF9QnYWlE9ylNdO4pS9VZ2HpBHdpHQ/aT0yCu1BEGoE3YHf0FmPXIfICK4BbnW3/6hxbg539th/YgJ1O/0D6d84tVGdh6QR3ac00bulL1VlYOsFdWseD9lN6MmJkiVMGQERuBSqwk4tdApQaY96VdNzfY1uw3zR2zhDELpg5NRH4lsuozsLSCe7Smmnc0peqs7B0gru0jgftp5Mz4e5Cx79a7Lyswk4a9ivgs8AbxcmJ4XAvEAK+LiIfEpGgMaY3HzpddRaWTnCX1kzjlr5UnYWlE9yldTxoP42NCTWyROQ84F+wO3IZtkXbAGCMOQZ8Dvh60ileYCrwEvBjY8zARLYnU6jOwtIJ7tKaadzSl6qzsHSCu7SOB+2nsTMh7kIRKQPuAHZgVw//HnZntgMfNsYkqmUHsROK/YMxZqOI1AFFxpj9427EJKA6C0snuEtrpnFLX6rOwtIJ7tI6HrSfTp2JmsmKY+e7+LOxa699HlhrjPk2EBGRjznH1WKnx98CYIw5mmedrjoLSye4S2umcUtfqs7C0gnu0joetJ9OkYnKk9UL3GeM2Ze07Wnn7z8D14idGr8TWG/yqO5QCqqzsHSCu7RmGrf0peosLJ3gLq3jQfvpFJkQI8sYEweSO70BJ+cFdq6MLwP1wB7HX5uXqE6ggHSCu7RmGrf0peoECkgnuEvreNB+OnUylfF9OnBcRH6BXQjyIWNM00nOyUdUZ+HhJq2Zxi19qToLDzdpHQ/aTydhwvNkicg04ClgI/ArY8zPJvQDcgTVWXi4SWumcUtfqs7Cw01ax4P209jIxExWDPg+8PUCX6apOgsPN2nNNG7pS9VZeLhJ63jQfhoDGS+royiKoiiK4kayUSBaURRFURSl4FEjS1EURVEUJQOokaUoiqIoipIB1MhSFEVRFEXJAGpkKYqiKIqiZAA1shRFURRFUTKAGlmKoiiKoigZ4P8AVCsnfjWywWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['MidClose', 'SMA1', 'SMA2']].plot(figsize=(10, 6));\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_02.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fxcmpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.6'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fxcmpy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "api = fxcmpy.fxcmpy(config_file='../../cfg/fxcm.cfg')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruments = api.get_instruments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF', 'EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD', 'EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'CHF/JPY', 'GBP/CHF', 'EUR/AUD', 'EUR/CAD', 'AUD/CAD', 'AUD/JPY', 'CAD/JPY', 'NZD/JPY', 'GBP/CAD', 'GBP/NZD', 'GBP/AUD', 'AUD/NZD', 'USD/SEK', 'EUR/SEK', 'EUR/NOK', 'USD/NOK', 'USD/MXN', 'AUD/CHF', 'EUR/NZD', 'USD/ZAR', 'USD/HKD', 'ZAR/JPY', 'USD/TRY', 'EUR/TRY', 'NZD/CHF', 'CAD/CHF', 'NZD/CAD', 'TRY/JPY', 'USD/ILS', 'USD/CNH', 'AUS200', 'ESP35', 'FRA40', 'GER30', 'HKG33', 'JPN225', 'NAS100', 'SPX500', 'UK100', 'US30', 'Copper', 'CHN50', 'EUSTX50', 'USDOLLAR', 'US2000', 'USOil', 'UKOil', 'SOYF', 'NGAS', 'USOilSpot', 'UKOilSpot', 'WHEATF', 'CORNF', 'Bund', 'XAU/USD', 'XAG/USD', 'EMBasket', 'JPYBasket', 'BTC/USD', 'BCH/USD', 'ETH/USD', 'LTC/USD', 'XRP/USD', 'CryptoMajor', 'EOS/USD', 'XLM/USD', 'ESPORTS', 'BIOTECH', 'CANNABIS', 'FAANG', 'CHN.TECH', 'CHN.ECOMM', 'USEquities']\n"
     ]
    }
   ],
   "source": [
    "print(instruments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Historical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('USD/JPY', period='D1', number=10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-15 21:00:00</th>\n",
       "      <td>107.227</td>\n",
       "      <td>106.925</td>\n",
       "      <td>107.304</td>\n",
       "      <td>106.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16 21:00:00</th>\n",
       "      <td>106.925</td>\n",
       "      <td>107.204</td>\n",
       "      <td>107.396</td>\n",
       "      <td>106.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-17 21:00:00</th>\n",
       "      <td>107.204</td>\n",
       "      <td>107.008</td>\n",
       "      <td>107.356</td>\n",
       "      <td>106.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20 21:00:00</th>\n",
       "      <td>107.008</td>\n",
       "      <td>107.241</td>\n",
       "      <td>107.539</td>\n",
       "      <td>107.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21 21:00:00</th>\n",
       "      <td>107.241</td>\n",
       "      <td>106.787</td>\n",
       "      <td>107.362</td>\n",
       "      <td>106.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22 21:00:00</th>\n",
       "      <td>106.787</td>\n",
       "      <td>107.146</td>\n",
       "      <td>107.283</td>\n",
       "      <td>106.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23 21:00:00</th>\n",
       "      <td>107.146</td>\n",
       "      <td>106.853</td>\n",
       "      <td>107.223</td>\n",
       "      <td>106.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24 21:00:00</th>\n",
       "      <td>106.853</td>\n",
       "      <td>106.070</td>\n",
       "      <td>106.899</td>\n",
       "      <td>105.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26 21:00:00</th>\n",
       "      <td>106.078</td>\n",
       "      <td>106.002</td>\n",
       "      <td>106.127</td>\n",
       "      <td>105.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27 21:00:00</th>\n",
       "      <td>106.002</td>\n",
       "      <td>105.265</td>\n",
       "      <td>106.099</td>\n",
       "      <td>105.259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow\n",
       "date                                                    \n",
       "2020-07-15 21:00:00  107.227   106.925  107.304  106.660\n",
       "2020-07-16 21:00:00  106.925   107.204  107.396  106.827\n",
       "2020-07-17 21:00:00  107.204   107.008  107.356  106.933\n",
       "2020-07-20 21:00:00  107.008   107.241  107.539  107.007\n",
       "2020-07-21 21:00:00  107.241   106.787  107.362  106.673\n",
       "2020-07-22 21:00:00  106.787   107.146  107.283  106.705\n",
       "2020-07-23 21:00:00  107.146   106.853  107.223  106.705\n",
       "2020-07-24 21:00:00  106.853   106.070  106.899  105.676\n",
       "2020-07-26 21:00:00  106.078   106.002  106.127  105.905\n",
       "2020-07-27 21:00:00  106.002   105.265  106.099  105.259"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[:4]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-15 21:00:00</th>\n",
       "      <td>107.243</td>\n",
       "      <td>106.945</td>\n",
       "      <td>107.317</td>\n",
       "      <td>106.672</td>\n",
       "      <td>171814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16 21:00:00</th>\n",
       "      <td>106.945</td>\n",
       "      <td>107.284</td>\n",
       "      <td>107.407</td>\n",
       "      <td>106.839</td>\n",
       "      <td>173810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-17 21:00:00</th>\n",
       "      <td>107.284</td>\n",
       "      <td>107.059</td>\n",
       "      <td>107.379</td>\n",
       "      <td>106.945</td>\n",
       "      <td>135795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20 21:00:00</th>\n",
       "      <td>107.059</td>\n",
       "      <td>107.276</td>\n",
       "      <td>107.553</td>\n",
       "      <td>107.022</td>\n",
       "      <td>159784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21 21:00:00</th>\n",
       "      <td>107.276</td>\n",
       "      <td>106.803</td>\n",
       "      <td>107.373</td>\n",
       "      <td>106.685</td>\n",
       "      <td>176453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22 21:00:00</th>\n",
       "      <td>106.803</td>\n",
       "      <td>107.163</td>\n",
       "      <td>107.295</td>\n",
       "      <td>106.718</td>\n",
       "      <td>174734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23 21:00:00</th>\n",
       "      <td>107.163</td>\n",
       "      <td>106.871</td>\n",
       "      <td>107.236</td>\n",
       "      <td>106.717</td>\n",
       "      <td>145894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24 21:00:00</th>\n",
       "      <td>106.871</td>\n",
       "      <td>106.180</td>\n",
       "      <td>106.923</td>\n",
       "      <td>105.687</td>\n",
       "      <td>240193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26 21:00:00</th>\n",
       "      <td>106.185</td>\n",
       "      <td>106.093</td>\n",
       "      <td>106.206</td>\n",
       "      <td>105.996</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27 21:00:00</th>\n",
       "      <td>106.093</td>\n",
       "      <td>105.275</td>\n",
       "      <td>106.132</td>\n",
       "      <td>105.270</td>\n",
       "      <td>142401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     askopen  askclose  askhigh   asklow  tickqty\n",
       "date                                                             \n",
       "2020-07-15 21:00:00  107.243   106.945  107.317  106.672   171814\n",
       "2020-07-16 21:00:00  106.945   107.284  107.407  106.839   173810\n",
       "2020-07-17 21:00:00  107.284   107.059  107.379  106.945   135795\n",
       "2020-07-20 21:00:00  107.059   107.276  107.553  107.022   159784\n",
       "2020-07-21 21:00:00  107.276   106.803  107.373  106.685   176453\n",
       "2020-07-22 21:00:00  106.803   107.163  107.295  106.718   174734\n",
       "2020-07-23 21:00:00  107.163   106.871  107.236  106.717   145894\n",
       "2020-07-24 21:00:00  106.871   106.180  106.923  105.687   240193\n",
       "2020-07-26 21:00:00  106.185   106.093  106.206  105.996      337\n",
       "2020-07-27 21:00:00  106.093   105.275  106.132  105.270   142401"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[4:]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2017, 1, 1)  \n",
    "end = dt.datetime(2018, 1, 1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/GBP', period='D1',\n",
    "                          start=start, stop=end)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 309 entries, 2017-01-03 22:00:00 to 2018-01-01 22:00:00\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   bidopen   309 non-null    float64\n",
      " 1   bidclose  309 non-null    float64\n",
      " 2   bidhigh   309 non-null    float64\n",
      " 3   bidlow    309 non-null    float64\n",
      " 4   askopen   309 non-null    float64\n",
      " 5   askclose  309 non-null    float64\n",
      " 6   askhigh   309 non-null    float64\n",
      " 7   asklow    309 non-null    float64\n",
      " 8   tickqty   309 non-null    int64  \n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 24.1 KB\n"
     ]
    }
   ],
   "source": [
    "candles.info()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter `period` must be one of `m1, m5, m15, m30, H1, H2, H3, H4, H6, H8, D1, W1` or `M1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/USD', period='m1', number=250)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8529b3ef50>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candles['askclose'].plot(figsize=(10, 6))\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_03.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output(data, dataframe):\n",
    "    print('%3d | %s | %s | %6.5f, %6.5f' \n",
    "          % (len(dataframe), data['Symbol'],\n",
    "             pd.to_datetime(int(data['Updated']), unit='ms'), \n",
    "             data['Rates'][0], data['Rates'][1]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2 | EUR/USD | 2020-07-27 10:29:36.921000 | 1.17302, 1.17314\n",
      "  3 | EUR/USD | 2020-07-27 10:29:37.323000 | 1.17300, 1.17312\n",
      "  4 | EUR/USD | 2020-07-27 10:29:37.775000 | 1.17299, 1.17311\n",
      "  5 | EUR/USD | 2020-07-27 10:29:38.799000 | 1.17301, 1.17314\n",
      "  6 | EUR/USD | 2020-07-27 10:29:39.333000 | 1.17299, 1.17311\n",
      "  7 | EUR/USD | 2020-07-27 10:29:39.649000 | 1.17300, 1.17313\n",
      "  8 | EUR/USD | 2020-07-27 10:29:40.158000 | 1.17301, 1.17313\n"
     ]
    }
   ],
   "source": [
    "api.subscribe_market_data('EUR/USD', (output,))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  9 | EUR/USD | 2020-07-27 10:29:40.479000 | 1.17300, 1.17313\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Bid     1.17300\n",
       "Ask     1.17313\n",
       "High    1.17339\n",
       "Low     1.16409\n",
       "Name: 2020-07-27 10:29:40.479000, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_last_price('EUR/USD')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.unsubscribe_market_data('EUR/USD')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placing Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "api.get_open_positions()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/USD', 100)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = ['tradeId', 'amountK', 'currency',\n",
    "       'grossPL', 'isBuy']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -11.08222   True"
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    "api.get_open_positions()[sel]  "
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -19.60868   True\n",
       "1  168818204       50  EUR/GBP -12.06709   True"
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     "execution_count": 49,
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    "api.get_open_positions()[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_sell_order('EUR/USD', 25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -11.93480   True\n",
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       "3  168818208       50  EUR/GBP -19.74810   True"
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     "execution_count": 52,
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   "source": [
    "api.get_open_positions()[sel]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all_for_symbol('EUR/GBP')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -14.49263   True\n",
       "1  168818206       25  EUR/USD  -4.04896  False"
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   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all()  "
   ]
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  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "api.get_open_positions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Account Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1233279"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_default_account()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>t</th>\n",
       "      <td>6</td>\n",
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       "      <th>ratePrecision</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accountId</th>\n",
       "      <td>1233279</td>\n",
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       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>49665.4</td>\n",
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       "    <tr>\n",
       "      <th>usdMr</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>mc</th>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcDate</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accountName</th>\n",
       "      <td>01233279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usdMr3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hedging</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMarginPerc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3Perc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equity</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayPL</th>\n",
       "      <td>-85.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grossPL</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "t                         6\n",
       "ratePrecision             0\n",
       "accountId           1233279\n",
       "balance             49665.4\n",
       "usdMr                     0\n",
       "mc                        N\n",
       "mcDate                     \n",
       "accountName        01233279\n",
       "usdMr3                    0\n",
       "hedging                   Y\n",
       "usableMargin3       49665.4\n",
       "usableMarginPerc        100\n",
       "usableMargin3Perc       100\n",
       "equity              49665.4\n",
       "usableMargin        49665.4\n",
       "bus                    1000\n",
       "dayPL                -85.34\n",
       "grossPL                   0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_accounts().T  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
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